diff --git a/.DS_Store b/.DS_Store index 9d98098..930adfe 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/.gitignore b/.gitignore index df80679..86bc903 100644 --- a/.gitignore +++ b/.gitignore @@ -87,4 +87,6 @@ ENV/ .spyderproject # Rope project settings -.ropeproject \ No newline at end of file +.ropeproject + +.DS_Store diff --git a/notebook1-4_test/american_flag_brexit.png b/notebook1-4_test/american_flag_brexit.png deleted file mode 100644 index 35cf367..0000000 Binary files a/notebook1-4_test/american_flag_brexit.png and /dev/null differ diff --git a/notebook1-4_test/flag_norway.png b/notebook1-4_test/flag_norway.png new file mode 100644 index 0000000..1f6a426 Binary files /dev/null and b/notebook1-4_test/flag_norway.png differ diff --git a/notebook1-4_test/notebook1-4_test.ipynb b/notebook1-4_test/notebook1-4_test.ipynb index 21cfd80..98e4bc0 100644 --- a/notebook1-4_test/notebook1-4_test.ipynb +++ b/notebook1-4_test/notebook1-4_test.ipynb @@ -31,6 +31,17 @@ "Use `plt.axis('scaled')` to make sure your squares look like squares and not rectangles." ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, { "cell_type": "code", "execution_count": null, @@ -97,11 +108,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Exercise 4\n", - "In the image below, you can see a possible future for the American flag if some of the American states leave the Union like the Brexit of England. Create a matrix of 13 rows and 20 columns. Create the red and white stripes plus the blue rectangle by assigning 0 (blue), 1 (white), and 2 (red) using at most three assignent statements. Show the matrix to the screen using the `bwr` colormap, and add the row of 9 stars by plotting markers with the `plt.plot` statement. \n", + "### Exercise 4\n", + "Create the flag of Norway as shown in the figure below.\n", + "\n", + "\n", "\n", - "Finally, add the line `plt.axis('image')` to your script, so the flag covers up your entire plot (no white banners). \n", - "![](american_flag_brexit.png)" + "Create an array of size 40 by 55. Fill the array with zeros, ones and two-s to represent the blue, white, and red parts, respectively. You may only use a maximum of 5 assignment statements to set the correct values for the colors. Make sure you plot the flag using the colormap `bwr` (which stands for blue-white-red)." ] }, { @@ -123,7 +135,7 @@ "metadata": {}, "source": [ "### Exercise 5\n", - "Write a function that computes the percentage of grades that is above a given value. The function takes as input arguments an array with grades between 1 and 10 and a minimum value and returns the precentage of grades (so between 0% and 100%) that are above or equal to that value. Demonstrate that your function works by loading the grades in the file `schoolgrades2016.txt` and print the result of the function to the screen, given a minimum value of 7." + "Write a function that computes the percentage of grades that is above a given value. The function takes as input arguments an array with grades between 1 and 10 and a minimum value and returns the precentage of grades (so between 0% and 100%) that are above or equal to that value. Demonstrate that your function works by loading the grades in the file `schoolgrades2016.txt` and print the result of the function to the screen with two decimal places, given a minimum value of 7." ] }, { @@ -171,7 +183,7 @@ "\n", "Answer to Exercise 4\n", "\n", - "Your graph should look like the provide figure\n", + "Your graph should look like the provided figure\n", "\n", "Back to Exercise 4\n", "\n", @@ -181,6 +193,13 @@ "\n", "Back to Exercise 5" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -200,7 +219,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/notebook10_continuous_random_variables/py_exploratory_comp_10_sol.ipynb b/notebook10_continuous_random_variables/py_exploratory_comp_10_sol.ipynb index a7ae296..8b7d524 100755 --- a/notebook10_continuous_random_variables/py_exploratory_comp_10_sol.ipynb +++ b/notebook10_continuous_random_variables/py_exploratory_comp_10_sol.ipynb @@ -43,8 +43,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "mean of data: 4.725073752687233\n", - "standard deviation of data: 1.9293057006952723\n" + "mean of data: 5.305482020173405\n", + "standard deviation of data: 2.176870849722937\n" ] } ], @@ -80,14 +80,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "number of data points in each bin: [ 2. 4. 6. 23. 11. 15. 18. 14. 3. 4.]\n", - "limits of the bins: [ 1.45684107 2.39440909 3.33197711 4.26954514 5.20711316 6.14468119\n", - " 7.08224921 8.01981724 8.95738526 9.89495329 10.83252131]\n" + "number of data points in each bin: [ 2. 3. 13. 20. 23. 18. 11. 7. 1. 2.]\n", + "limits of the bins: [ 1.03555067 2.11317224 3.19079381 4.26841538 5.34603695 6.42365852\n", + " 7.50128009 8.57890167 9.65652324 10.73414481 11.81176638]\n" ] }, { "data": { - "image/png": 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+ "image/png": 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8U5DkoUk+kGRzko8lOW/omlpIckCSjyZ5z9C1tJDksCTvSPIv3X/L7xq6pmlK8ovdn8+bk1ya5KCha9pfSS5Ksi3JzUvajkhyVZJbuufDh6xxfy1zjK/q/pzelOTyJIfty28a/NOxE3h5VT0KeALw4jldhuI8YPPQRTT0euB9VfWtwInM0bEmeTBwLrBYVY9mMrDix4etaiouBk7frW0tcHVVHQ9c3W2vZhez5zFeBTy6qh4D/CvwK/vygwb/FFTV1qr6SPd6B5PAePCwVU1XkocAZwAXDF1LC0kOBb4XuBCgqr5cVf81bFVTdyDwDUkOBB7AHMyfqaprgc/t1nwmsL57vR541kyLmrK9HWNVvb+qdnabH2IyH6o3g3/KkiwAjwWuH7aSqXsd8EvAPUMX0sjDge3AW7vurAuSHDx0UdNSVZ8F/gD4NLAV+HxVvX/Yqpo5uqq2wuSkDHjgwPW09jzgr/flCwb/FCX5RuCdwEur6s6h65mWJM8AtlXVxqFraehA4GTgTVX1WOCLrP4ugv/T9XOfCRwHPAg4OMlzh61K+yvJK5h0NV+yL98z+Kckyf2YhP4lVXXZ0PVM2ROBZybZwmQ11ackefuwJU3dbcBtVbXrX2rvYPIXwbz4fuBTVbW9qr4CXAZ898A1tXJ7kmMAuudtA9fTRJKzgWcAP1n7OCHL4J+CJGHSN7y5ql4zdD3TVlW/UlUPqaoFJhcE/66q5upssar+HfhMkkd2TacC/zxgSdP2aeAJSR7Q/Xk9lTm6eL2bK4Czu9dnA+8esJYmkpwO/DLwzKq6a1+/b/BPxxOBn2JyJnxD93j60EVpn70EuCTJTcBJwO8OXM/UdP+SeQfwEWATk//3V/3SBkkuBf4ReGSS25I8HzgfOC3JLcBp3faqtcwx/hFwCHBVlzdv3qffdMkGSRoXz/glaWQMfkkaGYNfkkbG4JekkTH4JWlkDH5pGUkWlq6IuKT9gjldhE8jMfNbL0qrXVX97NA1SPvDM35pZQcmWd+te/6ObubrNUkWAZJ8IcnvJLkxyYeSHN21P6db9/7GJNcOewjSVzP4pZU9EljXrXt+J/Ci3d4/GPhQVZ0IXAu8oGv/DeAHuvZnzqpYqQ+DX1rZZ6rqH7rXbweetNv7XwZ23ZFsI7DQvf4H4OIkL2By0xPpa4bBL61s9zVNdt/+ypKVEe+mu25WVT8P/BrwUOCGJN/ctEppHxj80sqOXXLv3bOA6/p8Kckjqur6qvoN4A4mfwFIXxMMfmllm4GzuxU7jwDe1PN7r0qyqRsOei1wY6sCpX3l6pySNDKe8UvSyBj8kjQyBr8kjYzBL0kjY/BL0sgY/JI0Mga/JI3M/wJhap1oJu2gEAAAAABJRU5ErkJggg==\n", 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" ] @@ -111,7 +111,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As you can see from the previous example, the limits of the bins are not chosen as nice numbers: `hist` takes the minimum and maximum value of the data and divides it in `nbin` equal intervals. You normally want to specify the number of bins with the `bins` keyword, and the range (minimum and maximum limits of the bins) with the `range` keyword. If data values are outside this range (such as outliers), they are ignored. In the code below, 12 bins are chosen equally spaced from 0 to 12. Note that we use the same date as for the graph above, but by simply choosing different bins the histogram looks quite different." + "As you can see from the previous example, the limits of the bins are not chosen as nice numbers: `hist` takes the minimum and maximum value of the data and divides it in `nbin` equal intervals. You normally want to specify the number of bins with the `bins` keyword, and the range (minimum and maximum limits of the bins) with the `range` keyword. If data values are outside this range (these may or may not be outliers), they are ignored. In the code below, 12 bins are chosen equally spaced from 0 to 12. Note that we use the same date as for the graph above, but by simply choosing different bins the histogram looks quite different." ] }, { @@ -123,13 +123,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "number of data points in each bin: [ 0. 1. 4. 7. 15. 15. 18. 19. 14. 4. 3. 0.]\n", + "number of data points in each bin: [ 0. 1. 3. 9. 15. 23. 23. 10. 12. 1. 2. 1.]\n", "limits of the bins: [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.]\n" ] }, { "data": { - "image/png": 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4k6FEI0maWC1PSfleDElS05DUo4F/z8Pfh/Hu+TSc5FbgPnoTAzdW1dS04wE+BBxHb6b5y6rqqvm0KUmau5YhqS8A9wBr6JvpvUCOmGWNqmOB/brPM4CPdt+SpDFoSRh7VdUxQ4/k4U4APl5VBVyRZJckj6+qO8YQiyQtei2LD349yb8bQtsFXJZkTZLlA47vCdzet7+uK3uIJMuTrE6y+q677hpCmJIkaOthPBt4WTfj+2dAgKqqp86z7cOqan2S3YGVSW6sqlV9xzPgnEEzzlcAKwCmpqYedlyStDBaEsaxw2i4qtZ33xuSXAgcCvQnjHXA3n37ewHrhxGLJGnzWmZ63zboM59Gk+yYZKdN28DRwPXTql0EvDQ9zwTu8f6FJI1PSw9jGPYALuw9Ocv2wKeq6tIkrwGoqjOBS+g9UnszvcdqTxlTrJIkxpQwquoW4KAB5Wf2bRfw+lHGJUmaWctTUpIkmTAkSW1MGJKkJiYMSVITE4Ykqcm4HqvVEC077eJxhyDNyyj+DN96+nOH3sa2xh6GJKmJCUOS1MSEIUlqYsKQJDUxYUiSmpgwJElNTBiSpCYmDElSExOGJKmJCUOS1MSlQSQtSqNaQmdbWoLEHoYkqcnIE0aSvZN8JcnaJDckOXVAncOT3JPk6u7zzlHHKUl6qHEMSW0E3lpVVyXZCViTZGVVfWdava9V1fFjiE+SNMDIexhVdUdVXdVt3wesBfYcdRySpC0z1nsYSZYBTwOuHHD4WUmuSfLFJE8ZaWCSpIcZ21NSSX4d+Czw5qq6d9rhq4B9qur+JMcBnwf2m+F3lgPLAZYuXTrEiCVpcRtLDyPJI+kli/Or6nPTj1fVvVV1f7d9CfDIJLsN+q2qWlFVU1U1tWTJkqHGLUmL2TiekgpwNrC2qt4/Q53f7OqR5FB6cf5wdFFKkqYbx5DUYcBfANclubor+w/AUoCqOhN4EfDaJBuBnwAnVlWNIVZJUmfkCaOqLgeymTpnAGeMJiJJUgtnekuSmpgwJElNTBiSpCYmDElSExOGJKmJCUOS1MSEIUlqYsKQJDUxYUiSmpgwJElNTBiSpCZjex/GYrTstIvHHYIkzZk9DElSExOGJKmJCUOS1MSEIUlqYsKQJDUxYUiSmowlYSQ5Jsl3k9yc5LQBxx+d5NPd8SuTLBt9lJKkfiNPGEm2Az4CHAscAJyU5IBp1V4B/Kiqngx8AHjPaKOUJE03jh7GocDNVXVLVf0c+AfghGl1TgDO67Y/AxyZJCOMUZI0zTgSxp7A7X3767qygXWqaiNwD/C4kUQnSRpoHEuDDOop1Bzq9Comy4Hl3e79Sb47x7h2A34wx3MnzbZyLdvKdYDXMolGch0ZzYD6fK5ln9aK40gY64C9+/b3AtbPUGddku2B3wDuHvRjVbUCWDHfoJKsrqqp+f7OJNhWrmVbuQ7wWibRtnIdMLprGceQ1LeA/ZLsm+RRwInARdPqXASc3G2/CPjHqhrYw5AkjcbIexhVtTHJG4AvAdsB51TVDUneDayuqouAs4FPJLmZXs/ixFHHKUl6qLEsb15VlwCXTCt7Z9/2T4EXjziseQ9rTZBt5Vq2lesAr2USbSvXASO6ljjSI0lq4dIgkqQmiz5hbG6Zkq1Fkr2TfCXJ2iQ3JDl13DHNV5Ltknw7yf8edyzzkWSXJJ9JcmP33+dZ445pLpL8Zfdn6/okFyTZYdwxtUpyTpINSa7vK9s1ycokN3Xfjx1njK1muJb3dn++rk1yYZJdhtH2ok4YjcuUbC02Am+tqt8Gngm8fiu+lk1OBdaOO4gF8CHg0qr6LeAgtsJrSrIn8CZgqqoOpPfAytb0MMq5wDHTyk4DvlxV+wFf7va3Bufy8GtZCRxYVU8F/hl4+zAaXtQJg7ZlSrYKVXVHVV3Vbd9H7y+l6TPotxpJ9gKeC5w17ljmI8nOwO/Te/KPqvp5Vf3beKOas+2BX+vmRj2Gh8+fmlhVtYqHz+XqX4LoPOAFIw1qjgZdS1Vd1q2KAXAFvfltC26xJ4yWZUq2Ot3qvk8DrhxvJPPyQeCvgF+OO5B5eiJwF/CxbnjtrCQ7jjuoLVVV3wf+K/AvwB3APVV12Xijmrc9quoO6P2DC9h9zPEslJcDXxzGDy/2hNG8BMnWIsmvA58F3lxV9447nrlIcjywoarWjDuWBbA9cAjw0ap6GvBjtp6hj/+vG98/AdgXeAKwY5KXjDcqTZfkHfSGp88fxu8v9oTRskzJViPJI+kli/Or6nPjjmceDgOen+RWesOEz0nyyfGGNGfrgHVVtam39xl6CWRr84fA96rqrqr6BfA54HfHHNN83Znk8QDd94YxxzMvSU4Gjgf+fFgrYyz2hNGyTMlWoVv+/WxgbVW9f9zxzEdVvb2q9qqqZfT+m/xjVW2V/5qtqn8Fbk+yf1d0JPCdMYY0V/8CPDPJY7o/a0eyFd68n6Z/CaKTgS+MMZZ5SXIM8Dbg+VX1wLDaWdQJo7tJtGmZkrXA/6iqG8Yb1ZwdBvwFvX+NX919jht3UALgjcD5Sa4FDgb+y5jj2WJdD+kzwFXAdfT+7thqZkonuQD4BrB/knVJXgGcDhyV5CbgqG5/4s1wLWcAOwEru//3zxxK2870liS1WNQ9DElSOxOGJKmJCUOS1MSEIUlqYsKQJDUxYUjzkGRZ/6qhfeVnbQOLP0oPMZY37knbuqp65bhjkBaaPQxp/rZPcl73LoLPdLOhv5pkCiDJ/Un+Jsk1Sa5IskdX/uLu3RLXJFk13kuQNs+EIc3f/sCK7l0E9wKvm3Z8R+CKqjoIWAW8qit/J/BHXfnzRxWsNFcmDGn+bq+qf+q2Pwk8e9rxnwOb3hq4BljWbf8TcG6SV9F7IZE00UwY0vxNX19n+v4v+lYPfZDu3mFVvQb4j/RWTL46yeOGGqU0TyYMaf6W9r2n+yTg8paTkjypqq6sqncCP+ChS+1LE8eEIc3fWuDkbjXaXYGPNp733iTXdY/lrgKuGVaA0kJwtVpJUhN7GJKkJiYMSVITE4YkqYkJQ5LUxIQhSWpiwpAkNTFhSJKamDAkSU3+H4hS8hzlSlqWAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -162,7 +162,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -188,7 +188,7 @@ "metadata": {}, "source": [ "### Exercise 1: First histogram\n", - "Generate 1000 random numbers from a Normal distribution with mean 100 and standard deviation 10. Compute and print to the screen the mean and standard deviation of your data. Create two graphs above each other using the `plt.subplot` command (use `plt.subplot?` if you forgot how to do that). In the top graph, plot a histogram using 20 bins going from 50 to 150. Note that with this size of a data set (1000 data points), the histogram starts to look a lot more like the typical bell-shaped curve of a Normal distribution. Add a red line representing the probability density function of the underlying normal distribution to the graph. In the bottom graph, draw a histogram of the cumulative distribution function, by setting the keyword `cumulative=True` (see `plt.hist?` for details). For the latter graph, use the keyword `align='right'` so that the bars are centered on the right bin edges (so that the line you are drawing next will approximately go through the centers of the bars). Add a red line representing the cumulative distribution function of the underlying normal distribution to the graph using the `norm.cdf` function, which works the same as the `norm.pdf` function but computes the cumulative distribution function (cdf). Finally, make sure the limits along the horizontal axis are the same for both graphs. " + "Generate 1000 random numbers from a Normal distribution with mean 100 and standard deviation 10. Compute and print to the screen the mean and standard deviation of your data. Create two graphs above each other using the `plt.subplot` command. In the top graph, plot a histogram using 20 bins going from 50 to 150. Note that with this size of a data set (1000 data points), the histogram starts to look a lot more like the typical bell-shaped curve of a Normal distribution. Add a red line representing the probability density function of the underlying normal distribution to the graph. In the bottom graph, draw a histogram of the cumulative distribution function, by setting the keyword `cumulative=True` (see `plt.hist?` for details). For the latter graph, use the keyword `align='right'` so that the bars are centered on the right bin edges (so that the line you are drawing next will approximately go through the centers of the bars). Add a red line representing the cumulative distribution function of the underlying normal distribution to the graph using the `norm.cdf` function, which works the same as the `norm.pdf` function but computes the cumulative distribution function (cdf). Finally, make sure the limits along the horizontal axis are the same for both graphs. " ] }, { @@ -209,8 +209,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Percentiles\n", - "Another useful description of a dataset is the percentiles or quantiles. For this we consider the ordered data, that is, we order the datapoints in ascending order (so the first datapoint is the minimum of the data and the last datapoint is the maximum). The 25 percentile is the data point in the ordered data such that 25% of the data is below this datapoint (and thus 75% is above this datapoint). The percentiles of a dataset are commonly referred to as the 'empirical percentiles' as they are the percentiles of the dataset, not of the underlying distribution. The 50 empirical percentile is equivalent to the median of the data. Common intervals to look at are the 50% region around the median (also called the interquartile range or IQR), which runs from the 25 empirical percentile to the 75 empirical percentile, and the 95% region, which runs from the 2.5 empirical percentile to the 97.5 empirical percentile. Percentiles of a dataset may be computed with the `percentile` function in the `numpy` package. The first argument is the data, the second argument is a list of percentiles:" + "### Quantiles\n", + "Another useful description of a dataset are the quantiles. Quantiles are computed using ordered data (in ascending order). The 25 quantile is the data point in the ordered data such that 25% of the data is below this datapoint (and thus 75% is above this datapoint). The quantiles of a dataset are commonly referred to as the 'empirical quantiles' as they are the quantiles of the dataset, not of the underlying distribution. Quantiles are specified from 0 (0%) to 1 (100%). The 0.5 empirical quantile is equivalent to the median of the data. Common intervals to look at are the 50% region around the median (also called the interquartile range or IQR), which runs from the 0.25 empirical quantile to the 0.75 empirical quantile, and the 95% region, which runs from the 0.025 empirical quantile to the 0.975 empirical quantile. Quantiles of a dataset may be computed with the `quantile` function in the `numpy` package. The first argument is the data, the second argument is a list of quantiles:" ] }, { @@ -222,19 +222,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.5 percentile: 6.005122064821269\n", - "50 percentile: 10.327944477985223\n", - "97.5 percentile: 14.048919837033209\n", - "95% interval: 6.005122064821269 to 14.048919837033209\n" + "0.025 quantile: 6.211266727003688\n", + "0.5 quantile: 10.101217629880178\n", + "0.975 quantile: 14.028302948537071\n", + "95% interval: 6.211266727003688 to 14.028302948537071\n" ] } ], "source": [ "data = rnd.normal(loc=10, scale=2, size=100)\n", - "lower, median, upper = np.percentile(data, [2.5, 50, 97.5])\n", - "print('2.5 percentile:', lower)\n", - "print('50 percentile:', median)\n", - "print('97.5 percentile:', upper)\n", + "lower, median, upper = np.quantile(data, [0.025, 0.5, 0.975])\n", + "print('0.025 quantile:', lower)\n", + "print('0.5 quantile:', median)\n", + "print('0.975 quantile:', upper)\n", "print('95% interval:', lower, ' to ', upper)" ] }, @@ -242,7 +242,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Theoretical percentiles of a given distribution may be computed with the `ppf` function, but now the percentiles need to be specified as values less than 1 (so 0.5 for the 50 percentile). For example, the theoretical values for the Normal distribution used above are" + "Theoretical quantiles of a given distribution may be computed with the `ppf` function (for percentage point function - odd name). For example, the theoretical values for the Normal distribution used above are" ] }, { @@ -270,7 +270,7 @@ "metadata": {}, "source": [ "### Expercise 2. Lower and upper quartile\n", - "Generate 100 data points from a normal distribution with a mean of 20 and a standard deviation of 4. Compute the interquartile range (25%-75% range). Compute the theoretical value of the interquartile range and compare it to the interquartile range of the data. Draw a histogram of the cumulative distribution. Add red vertical lines to your graph for the 25 and 75 empirical percentiles of the data, and black vertical lines for the 25 and 75 percentiles of the underlying distribution. Vertical lines that span the graph may be added with the `plt.axvline` function, which takes the $x$ value of the line as an argument. To specify the color of the vertical line, use the `color` keyword argument." + "Generate 100 data points from a normal distribution with a mean of 20 and a standard deviation of 4. Compute the interquartile range (25%-75% range). Compute the theoretical value of the interquartile range and compare it to the interquartile range of the data. Draw a histogram of the cumulative distribution. Add red vertical lines to your graph for the 0.25 and 0.75 empirical quantiles of the data, and black vertical lines for the 0.25 and 0.75 quantiles of the underlying distribution. Vertical lines that span the graph may be added with the `plt.axvline` function, which takes the $x$ value of the line as an argument. To specify the color of the vertical line, use the `color` keyword argument." ] }, { @@ -292,7 +292,7 @@ "metadata": {}, "source": [ "### Box-whisker plots\n", - "Box-whisker plots (also simply referred to as boxplots) are a way to visualize the level and spread of the data. From a boxplot, you can see whether the data is symmetric or not, and how widely the data are spread. A box-whisker plot may be created with the `boxplot` function in the `matplotlib` package as follows" + "Box-whisker plots (also simply referred to as boxplots) are a way to visualize the level and spread of the data. From a boxplot, you can see whether the data is symmetric or not, and how widely the data are spread. A box-whisker plot may be created with the `boxplot` function in the `matplotlib` package. As an example, a boxplot is drawn for 500 values drawn from a Normal distribution" ] }, { @@ -302,7 +302,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -323,7 +323,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `boxplot` function creates the graph and returns a lot of stuff such as 'boxes', 'caps', etc. These latter ones are handles to the different features of the graph; we will not use them here. What you see in the graph is a red line at the median of the data. The blue box spans the IQR ranging from the lower quartile (25%) to the upper quartile (75%). The whiskers are the black lines that are connected to the 50% box with black lines. They extend to the most extreme data point within the `whis*IQR` data range, where the default value of `whis` is 1.5. Any data points falling outside the whiskers are potential outliers and are plotted as little circles. In this case there are 5 points outside the whiskers, but none are outliers. They were, after all, drawn from the same Normal distribution!" + "The `boxplot` function creates the graph and returns a lot of stuff such as 'boxes', 'caps', etc. These latter ones are handles to the different features of the graph; we will not use them here. What you see in the graph is a red line at the median of the data. The box spans the IQR ranging from the lower quartile (25%) to the upper quartile (75%). The whiskers are the black lines that are connected to the IQR box with black lines. They extend to the most extreme data point within the `whis*IQR` data range, where the default value of `whis` is 1.5. Any data points falling outside the whiskers are potential outliers and are plotted as little circles. In this case there are 5 points outside the whiskers, but none are outliers. They were, after all, drawn from the same Normal distribution!" ] }, { @@ -333,7 +333,7 @@ "### Pandas\n", "All the techniques described in this Notebook can also be done with the `pandas` package. `pandas` is often much easier as it has a lot more functionality, it can handle missing values (`NaN` values, for example), and the plots look pretty by default.\n", "\n", - "The `read_csv` function of `pandas` may be used to read data from a file and store it in a `DataFrame` (see `pandas` Notebook). A `DataFrame` may also be created from scratch. First, the `pandas` package is imported and called `pd`. Then an empty `DataFrame` is created and values are added to two columns by drawing from two different normal distributions; the columns are called `test1` and `test2`. The `describe` function of `pandas` gives a nice summary of the data, including the number of values, mean, standard deviation, min, 25%, 50%, 75%, and max values. " + "The `read_csv` function of `pandas` may be used to read data from a file and store it in a `DataFrame` (see `pandas` Notebook). In the example below, a `DataFrame` is created from scratch. First, the `pandas` package is imported and called `pd`. Then an empty `DataFrame` is created and values are added to two columns by drawing from two different normal distributions; the columns are called `test1` and `test2`. The `describe` function of `pandas` gives a nice summary of the data, including the number of values, mean, standard deviation, min, 25%, 50%, 75%, and max values. " ] }, { @@ -368,42 +368,42 @@ " \n", " \n", " \n", - " count\n", + " count\n", " 100.000000\n", " 100.000000\n", " \n", " \n", - " mean\n", + " mean\n", " 2.862210\n", " 5.101536\n", " \n", " \n", - " std\n", + " std\n", " 1.883256\n", " 0.933086\n", " \n", " \n", - " min\n", + " min\n", " -2.632002\n", " 3.012896\n", " \n", " \n", - " 25%\n", + " 25%\n", " 1.487364\n", " 4.512289\n", " \n", " \n", - " 50%\n", + " 50%\n", " 2.742579\n", " 5.003217\n", " \n", " \n", - " 75%\n", + " 75%\n", " 4.158108\n", " 5.631319\n", " \n", " \n", - " max\n", + " max\n", " 8.343370\n", " 7.662577\n", " \n", @@ -440,7 +440,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Values such as `mean` or `max` may be obtained for the entire `DataFrame` or for one column at a time. The percentiles may be obtained with the `quantile` function. The `quantile` function returns a DataFrame, which may be accessed using the standard functions for a DataFrame, or the values may be extracted into a `numpy` array using the `.values` attribute." + "Values such as `mean` or `max` may be obtained for the entire `DataFrame` or for one column at a time. The quantiles may be obtained with the `quantile` function. The `quantile` function returns a DataFrame, which may be accessed using the standard functions for a `DataFrame`, or the values may be extracted into a `numpy` array using the `.values` attribute." ] }, { @@ -457,7 +457,7 @@ "test1 1.883256\n", "test2 0.933086\n", "dtype: float64\n", - "5% and 95% precentiles of test2:\n", + "5% and 95% quantiles of test2:\n", "0.05 3.535725\n", "0.95 6.821249\n", "Name: test2, dtype: float64\n", @@ -469,7 +469,7 @@ "print('minimum of test1:', data.test1.min())\n", "print('standard deviation of the DataFrame:')\n", "print(data.std())\n", - "print('5% and 95% precentiles of test2:')\n", + "print('5% and 95% quantiles of test2:')\n", "print(data.test2.quantile([0.05, 0.95]))\n", "print('quantiles as numpy array:', data.test2.quantile([0.05, 0.95]).values)" ] @@ -478,7 +478,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The histogram of the data in two columns may be created with the `hist` function of `pandas`. Notice that the `sharex` and `sharey` keywords are set to `True` so that the horizontal and vertical axes have the same limits for both histograms (which facilitates comparison). The figure size is specified so that the figure is wider (10) than high (4)." + "The histogram of the data in two columns may be created with the `hist` function of `pandas`. Notice that the `sharex` and `sharey` keywords are set to `True` so that the horizontal and vertical axes have the same limits for both histograms (which facilitates comparison). " ] }, { @@ -488,7 +488,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": "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\n", "text/plain": [ "
" ] @@ -508,7 +508,7 @@ "metadata": {}, "source": [ "### Missing data\n", - "Real data often contains missing values. Every database has its own way of treating missing values. Some databases leave the value empty, others substitute a number that can easily be recognized (for example -9999). In `pandas` these values need to be converted to *Not A Number* using the `NaN` value of the `numpy package` (both `np.NaN` and `np.nan` work). In the code below, the value with index 5 in the `test1` column is changed to `nan`. A cumulative histogram may be obtained as (Note: the `plt.hist` function doesn't work on data that includes NaN values, but the histogram function of `pandas` works like a charm). " + "Real data often contains missing values. Every database has its own way of treating missing values. Some databases leave the value empty, others substitute a number that can easily be recognized (for example -9999). In `pandas` these values need to be converted to NaNs (Not A Number). In the code below, the value with index 5 in the `test1` column is changed to `nan`. A cumulative histogram may be obtained as (Note: the `plt.hist` function doesn't work on data that includes NaN values, but the histogram function of `pandas` works like a charm). " ] }, { @@ -518,7 +518,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -548,7 +548,7 @@ "outputs": [ { "data": { - "image/png": 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AAABJRU5ErkJggg==\n", 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" ] @@ -577,7 +577,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": "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\n", 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" ] @@ -652,7 +652,7 @@ "\n", "* Determine and report the minimum and maximum measured values of the bending strength. \n", "* Determine and report the mean and standard deviation of the density. \n", - "* Determine and report the 2.5, 50, and 97.5 percentiles of the tree ring width." + "* Determine and report the 0.25, 0.5, and 0.975 quantiles of the tree ring width." ] }, { @@ -710,7 +710,7 @@ "metadata": {}, "source": [ "### Exercise 5. Histogram of bending strength\n", - "Create a histogram of the bending strength. Add labels to the axes. Does the histogram look like a Normal distribution? On the same graph draw a red vertical line for the experimentally determined 5% bending strength. Print the 5 percentile bending strength to the screen." + "Create a histogram of the bending strength. Add labels to the axes. Does the histogram look like a Normal distribution? On the same graph draw a red vertical line for the experimentally determined 5% bending strength. Print the 0.05 experimental quantile bending strength to the screen." ] }, { @@ -778,7 +778,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -854,7 +854,7 @@ "p25, p75 = norm.ppf([0.25, 0.75], loc=mu, scale=sig)\n", "print('IQR pdf:', p25, p75)\n", "data = rnd.normal(loc=mu, scale=sig, size=100)\n", - "d25, d75 = np.percentile(data, [25, 75])\n", + "d25, d75 = np.quantile(data, [0.25, 0.75])\n", "print('IQR of data ', d25, d75)\n", "plt.hist(data, bins=20, cumulative=True, density=True, align='right')\n", "plt.axvline(d25, color='r')\n", @@ -926,7 +926,7 @@ }, { "data": { - "image/png": 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+ "image/png": "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\n", "text/plain": [ "
" ] @@ -950,7 +950,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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" ] @@ -984,12 +984,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "5 empirical percentile: 22.412499999999998\n" + "0.05 empirical quantile: 22.412499999999998\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1004,7 +1004,7 @@ "w.hist(column='bstrength', density=True)\n", "plt.xlabel('bending strength (N/m$^2$)')\n", "five = w.bstrength.quantile(0.05)\n", - "print('5 empirical percentile: ', five)\n", + "print('0.05 empirical quantile: ', five)\n", "plt.axvline(five, color='r');" ] }, @@ -1024,7 +1024,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1071,7 +1071,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.4" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false } }, "nbformat": 4, diff --git a/notebook11_hypothesis_test/py_exploratory_comp_11_sol.ipynb b/notebook11_hypothesis_test/py_exploratory_comp_11_sol.ipynb index 0b04c05..39f8d33 100644 --- a/notebook11_hypothesis_test/py_exploratory_comp_11_sol.ipynb +++ b/notebook11_hypothesis_test/py_exploratory_comp_11_sol.ipynb @@ -23,9 +23,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -44,19 +42,17 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "mean a: 3.9808416014\n", - "mean a: 4.10028352882\n", - "mean a: 3.94648379453\n", - "mean a: 4.23661749294\n", - "mean a: 4.1778813567\n" + "mean a: 3.5749106487481073\n", + "mean a: 3.9357430095703756\n", + "mean a: 3.9711565636741524\n", + "mean a: 3.8555191640408335\n", + "mean a: 3.873414227135426\n" ] } ], @@ -96,9 +92,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -147,20 +141,18 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "5% limit: -1.64485362695\n", - "95% limit: 1.64485362695\n", - "check if it works for 5%: 0.05\n", + "5% limit: -1.6448536269514729\n", + "95% limit: 1.6448536269514722\n", + "check if it works for 5%: 0.049999999999999975\n", "check if it works for 95%: 0.95\n", - "5% limit with mu=20, sig=10: 3.55146373049\n", - "check: 0.05\n" + "5% limit with mu=20, sig=10: 3.5514637304852705\n", + "check: 0.049999999999999975\n" ] } ], @@ -188,18 +180,16 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "5% limit: 16.630249761\n", - "95% limit: 23.369750239\n", - "check if it works for 5%: 0.0500000002153\n", - "check if it works for 95%: 0.949999999785\n" + "5% limit: 16.6302497610052\n", + "95% limit: 23.369750238994797\n", + "check if it works for 5%: 0.050000000215266564\n", + "check if it works for 95%: 0.9499999997847333\n" ] } ], @@ -224,9 +214,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -248,9 +236,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -264,9 +250,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -282,15 +266,13 @@ "metadata": {}, "source": [ "### Exercise 5. Hypothesis tests on Wooden beam data\n", - "Load the data set of experiments on wooden beams stored in the file `douglas_data.csv`. First, consider the first 20 measurements of the bending strength. Compute the sample mean and the standard deviation of the sample mean. The manufacturer claims that the mean bending strength is only 50 Pa. Perform a $t$-test (significance level 5%) with null hypothesis that the mean is indeed 50 Pa and alternative hypothesis that the mean is not 50 Pa using the approach applied in Exercise 4." + "Load the data set of experiments on wooden beams stored in the file `douglas_data.csv`. First, consider the first 20 measurements of the bending strength. Compute the sample mean and the standard deviation of the sample mean. The manufacturer claims that the mean bending strength is only 50 N/mm$^2$. Perform a $t$-test (significance level 5%) with null hypothesis that the mean is indeed 50 N/mm$^2$ and alternative hypothesis that the mean is not 50 N/mm$^2$ using the approach applied in Exercise 4." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -304,9 +286,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -328,26 +308,26 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "95 percentile Standard Normal: 1.64485362695\n", - "95 percentile t-dist with n=99: 1.660391156\n" + "95 percentile Standard Normal: 1.6448536269514722\n", + "95 percentile t-dist with n=99: 1.6603911559963895\n" ] }, { "data": { - "image/png": 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O/qDBtqOoU8jsM5iWeWtJeHuj7Siqgrlzxt8B2GaM2WGMyQVmAb2LbmCMOWiM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\n", 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DhMSe/WiXvpCfPtyde3tj3GSF3hRrJ05Ao5Wfsqd8I/xbNnM6zkWr8ezd+KAc+XfOc5Eakx9W6E2xNv8/O7hCl5J2590eMSfsxfKpW5vYyKtoteVzDh1yOo3xFlboTbF2YtI0AGo81cvhJAXHv19vGrGeBePXOR3FeAkr9KbYOnwYWmyZRmy1K5Gomk7HKTBVB/ckHR+SP5nmdBTjJazQm2JrwTvracyf+N7lPWfzAFKpIrF1bqDNrmnsifes+SJM8eRWoReRTiKyWUS2iciwHLYPFJE/RWS1iCwWkYZZtj3j2m+ziHQsyPCmZEv8+EvS8SFySPEb8iA3pe/vTV2287/RK5yOYrxAroXeNefrBKAz0BDonbWQu0xR1caq2gx4Axjt2rchmXPMXgZ0At49M4esMRdj317lirhpxNW6DqlS2ek4Ba7yQ91JFX90qnXfmIvnzhl9K2Cbqu5Q1RRgGtAtawNVzTplQmngzOfNbsA0VU1W1Z3ANtfxjLkoi8asoj5bCb7Xu7pt/hYWRmyDzrTb/yU7t9vgN+biuFPoqwFZn96Id607i4g8IiLbyTyjH5zHfQeISIyIxNhkwMYdGV9MJRU/qjzSw+kohSbswV5UJ54lbyxxOoop5twp9DndnHzOFSJVnaCqdYCngefzuO8kVY1W1eiIiAg3IpmSLG5nBlfv+5K4SzpC+fJOxyk0Efd1IcknGP+Z1n1jLo47hT4eqJ5lORLYe4H204Bb87mvMblaMvp3arCbMgO8tNvmjJAQdjXpwrWHZ7B1k01IYvLPnUK/HKgnIrVEJIDMi6uzszYQkXpZFm8Gzkx+ORvoJSKBIlILqAf8cfGxTUkmM74iRQKoeH8Xp6MUuvCBt1OJg/zx9q9ORzHFWK6FXlXTgEHAXGAjMF1V14vISBHp6mo2SETWi8hqYCjQ37XvemA6sAH4L/CIqtqpicm33XEZtN0/I7PbphhOMJJX4X1vItGnFP7ffOV0FFOMuTWmq6r+APyQbd0LWb5+9AL7vgq8mt+AxmS1ZOxyerGb/fe+4nSUolGqFLsb3cTVa2exfct46tS3u5NN3tmTsaZYyZj+FaniT+UBXXNv7CXKP3g7VdjPsrft7huTP1boTbGxJ165cu8MYut3gLAwp+MUmQr9biJJgvCz7huTT1boTbHx65gYoogjpH9Pp6MUrZAQdl12E1cfnGkPT5l8sUJvio2M6V9lPiQ1sFvujb1M2D96UpV9LBv9m9NRTDFkhd4UC/v2KlfEzyCubnsoV87pOEWu4n23kCyByMwZTkcxxZAVelMsLH5nJbXZSXA/7xup0i2hocQ16MRVB2awO866b0zeWKE3xULKtFmk4UvVh0pet80Zof1vI5I9/DZuudNRTDFjhd54vMOHlMtjZxIbdS1SIdzpOI6pMqALqfiR8dVMp6OYYsYKvfF4v0zcyKVsxv8O7x2p0i1hYcTWuYGW8bM4eMBmnjLus0JvPN6pz2aRgVDj0e5OR3FcUJ/bqMt2Fr/3p9NRTDFihd54tJMnodGWmcRVaYNUreJ0HMdFPtKNdHxI/MK6b4z7rNAbj/a/j3bQTFejPW5zOopHkEoVia1+NU23zeLYMafTmOLCCr3xaEc/nAVAzSHWbXOGb88eNGIdv3ywxekoppiwQm88VlIS1Fs3i7gKLfCtW8vpOB7jzLWKEx/PcjiJKS6s0BuP9eu0PVyRsZSUm0v43TbZ+NSsTmzFVjTYMJPTp51OY4oDK/TGYx2Y+A0ANR+zQp9dWrfbuFxj+OXzXU5HMcWAW4VeRDqJyGYR2SYiw3LYPlRENojIWhGZLyI1s2xLF5HVrtfs7Psak5O0NKi+4mv2lLmUgKYNnI7jcaIey+y+SfjPNw4nMcVBroVeRHyBCUBnoCHQW0QaZmu2CohW1SbADOCNLNsSVbWZ61VyZoswF2Xp94dpm7qIk+3tbD4nfg3qsTusEbVWzSI11ek0xtO5c0bfCtimqjtUNQWYBpw14IiqLlTVM72Fy4DIgo1pSpq4Cd/hR7p121zAqY49aJP+K0u+Puh0FOPh3Cn01YDdWZbjXevO537gxyzLQSISIyLLROTWnHYQkQGuNjEJCQluRDLeTBUqLv6ag8E1CG7bwuk4HitqSHd8ySD+XesRNRfmTqGXHNblONCGiNwNRANvZlldQ1WjgT7AWBGpc87BVCeparSqRkdERLgRyXizlb/8RbukuRy6qjtITj9+BiCodVMOlKpF5WVfk2EjF5sLcKfQxwPVsyxHAnuzNxKR9sBzQFdVTT6zXlX3uv67A1gENL+IvKYE2DzuvwSRTORg67a5IBEOX9uDq5N/Jmb+cafTGA/mTqFfDtQTkVoiEgD0As76rCgizYGJZBb5g1nWlxORQNfXFYC2wIaCCm+8U5n5szjqH0GZzm2djuLxqv+zO4GksG38D05HMR4s10KvqmnAIGAusBGYrqrrRWSkiJy5i+ZNIAT4KtttlA2AGBFZAywERqmqFXpzXpvWJNPuxPfsbdkNfH2djuPxQju04UhAZcIWfo3ayMXmPPzcaaSqPwA/ZFv3Qpav259nv9+AxhcT0JQsq0cv4FJOUulBG9vGLT4+7G/djXa/fs76mEQatQx2OpHxQPZkrPEoQT/M4pRPKBXuvMHpKMVG5Yd7EMIp1o2Z53QU46Gs0BuPsWtnOm0PfUNc41sgMNDpOMVG+R7XctI3jFL/tUHOTM6s0BuPsXzMYiI4RNh9drdNngQEsKtZF9oe/Y6dW+wxWXMuK/TGc8yaRZIEUfW+Tk4nKXbC/9GDcI4QM/oXp6MYD2SF3niEhINKyz1fs7NeRwgJcTpOsVO5XwcSfUrh+61135hzWaE3HuG3f6+gBrspdbd12+RLqVLsvKQzV+z/mgP77DFZczYr9MYjJE+dRSp+1Hj4FqejFFsh/XpQlX0sHfu701GMh7FCbxx38oTSbNtMdtS4Fgkv73ScYqv6wJtJwZ/U6V87HcV4GCv0xnFLPthIfbbg29O6bS6GhJVlR9QNNI+dxbGj9pis+X9W6I3jjk+eSQZCrSHdcm9sLsj/zh7UZTuL313rdBTjQazQG0clJUGDjTPZUflKfKtXdTpOsVfrsVtJx4fTn810OorxIFbojaMWf7KdJhlrSOt2m9NRvIJPpQi2V2tHoy0zOXXK6TTGU1ihN446/J/MM886T1j/fIHpcRsNdQNLPtjodBLjIazQG8ekpkLd1TPYER6Nf92aTsfxGrUfzxz58/hk674xmazQG8csm76Ly9OXk3STddsUJL+a1dhWsQ2XrJtJcnLu7Y33s0JvHLNvQubj+rWftEJf0FK73EaTjNX89vkOp6MYD+BWoReRTiKyWUS2iciwHLYPFZENIrJWROaLSM0s2/qLyFbXq39BhjfFV3o61IiZSVzZxgQ1rud0HK9T23XNI2Gidd8YNwq9iPgCE4DOQEOgt4g0zNZsFRCtqk2AGcAbrn3LAy8CrYFWwIsiUq7g4pviavl3+2mVuoQTHXo6HcUrBV5aix3lL6f2qpmkpTmdxjjNnTP6VsA2Vd2hqinANOCsJ1tUdaGqnnYtLgMiXV93BOap6hFVPQrMA2wMWsOucV/jg1L7Ceu2KSynO99GdNrvLPtqt9NRjMPcKfTVgKw/KfGudedzP/BjXvYVkQEiEiMiMQkJCW5EMsVZRgZE/jad3SGXUrpl9g+HpqDUeSrz09K+d2Y4nMQ4zZ1CLzmsy3EgDRG5G4gG3szLvqo6SVWjVTU6IiLCjUimOFvxwwFap/zC0RvvAMnpR8QUhOAm9dgZ1oyo5V+Rnu50GuMkdwp9PFA9y3IksDd7IxFpDzwHdFXV5Lzsa0qW2NGz8CWD2k/d7nQUr3eq8+20TFvK7zOs+6Ykc6fQLwfqiUgtEQkAegGzszYQkebARDKL/MEsm+YCHUSknOsibAfXOlNCZWRAtd+msyukASGtL3M6jter/XTmH9O94637piTLtdCrahowiMwCvRGYrqrrRWSkiHR1NXsTCAG+EpHVIjLbte8R4GUy/1gsB0a61pkSatWP+2md/AtH299u3TZFoFTTM9030637pgTzc6eRqv4A/JBt3QtZvm5/gX0nA5PzG9B4l7gxs7icDGo/fYfTUUqMk53uIHrasyybuYsr7qjhdBzjAHsy1hQZVaiy+Ct2hTQg9ArrtikqdYZZ901JZ4XeFJnV/91Pq+RfOHKDnc0XpdJN67KjbHNq/GF335RUVuhNkdn5dubdNrXsbpsid7LT7USnLiNmZpzTUYwDrNCbIpGRAdUWT2NXSEPKXmndNkWt7rOZn6L2jp3ucBLjBCv0pkis/HY3rZN/5Win3k5HKZFKN6nDtvItqb18mo19UwJZoTdFYvfbmWeSdZ7r5XCSkut0t940TVvJ759tcTqKKWJW6E2hS0+H2r9PZWu5loQ0q+t0nBKr/nN3kIFw6J2pTkcxRcwKvSl0f3yxlaZpKzjdxc7mnRRUpxqbK7WjwZqppCTnOFyV8VJW6E2hOzh+KhkI9Yff6XSUEi/t9t7Uz9jMsolrnI5iipAVelOoUpKVBqumsqliO4LrXmh0a1MULnm2J6n4cXyidd+UJFboTaH6fdIa6mdsIq2n3W3jCQKqhLOpegeabpzG6b8ynI5jiogVelOojr8/lVT8uORZm0nKU/je1YsauotlY5Y6HcUUESv0ptCcOplBs41T2Fi9I4HVKjgdx7hc8vStJBJM8uTPnY5iiogVelNolo1aRKTG439fX6ejmCx8w0LZeGl3Wsd+yeG9ybnvYIo9K/Sm0GR88hknpAyXPNk198amSJUd1JfyHCXmpTlORzFFwK1CLyKdRGSziGwTkWE5bG8nIitFJE1Eembblu6ajOTvCUmM9zuw8zRX7JnBlsY98Skd7HQck03tAe055FeJ4BmfOR3FFIFcC72I+AITgM5AQ6C3iDTM1mwXcA8wJYdDJKpqM9fLTu1KiJUvfksof1HhMeu28UTi78eO1n244sgcYlccdjqOKWTunNG3Arap6g5VTQGmAd2yNlDVWFVdC9j9WgaAst9+yj7/GkT1a+d0FHMeNZ7rSwCpbBhhI1p6O3cKfTUg6xTy8a517goSkRgRWSYit+YpnSmWtv66n9YnfmJXu7vBxy4DearKnZqxo3QjKv/8GWojIng1d34Lc5rBOS8/FjVUNRroA4wVkTrnvIHIANcfg5iEhIQ8HNp4oq0vT82cYOQF67bxaCIc6tSXFklLWTtzq9NpTCFyp9DHA9WzLEcCe919A1Xd6/rvDmAR0DyHNpNUNVpVoyMiItw9tPFAGelKzUWfsLlMNBXbXep0HJOLS0beRQbC3tc/dTqKKUTuFPrlQD0RqSUiAUAvwK27Z0SknIgEur6uALQFNuQ3rPF8yyeu5LLUNZzseZ/TUYwbyjasxtoqHWmy4mOSTtmEst4q10KvqmnAIGAusBGYrqrrRWSkiHQFEJGWIhIP3A5MFJH1rt0bADEisgZYCIxSVSv0XuzE2A9JJIhGr9rYNsWFzz/up5rG8/sr85yOYgqJqIddhYmOjtaYmBinY5h8OLYvEapWYUu9W2i1xR6vLy4yklI4Wroa6yOupd3+r5yOY/JJRFa4roeew26JMAVmxXOzCOM4ZR+/3+koJg98ggLY0vJurjjwLXtW280Q3sgKvSkwZWd8yO6A2tT/xzVORzF5VH3E/QSQyvpnv3A6iikEVuhNgdjy43aiTy5k1w33Ir72Y1XcRHZqxMbQVtT8+UMy0j2rO9dcPPuNNAUibuTHpOPDpa/d43QUk0/He97PJanrWP2BXSPzNlbozUVL/iuVRn9MZk2ljoQ3jXQ6jsmnJv/qxSlKceLNiU5HMQXMCr25aH8Mn02VjL3Iww85HcVchFKVy7C64V202j6Fw9uPOR3HFCAr9OaiBX/8Hnv8atD0mZucjmIuUqURD1GKRFY/bk/KehMr9OaibP1uE9HH5rO9/YP4+Ps6HcdcpLq3N2dd6BVE/fCuXZT1IlbozUXZM/x9UvCn4Vt277y3ONHnIeqkbmbV6IVORzEFxAq9ybdTB0/RfM3HxNS8jQqXVXI6jikgLUbdwREpT8q495yOYgqIFXqTbyufmnhwTGQAABSISURBVEZZjlP6iYedjmIKUFBYEGsvv4+We75m/0q3B6o1HswKvckXzVAipk9gS2Ajmjx8ldNxTAGLGjUQP9LZPNRutfQGVuhNvqx79xcuTVzF3u6DEJ+c5qYxxVnUDXX4PeJmLvv1PZKPJzkdx1wkK/QmX5L+NZrDEk7Ld/o5HcUUEp/Hh1IhI4EVQ238m+LOCr3Js/gFW7h833esbvMwpSsEOx3HFJLoJ69jU2BTKk0ZjWbYrZbFmRV6k2dxQ8eRij+XvvOI01FMIRIfYV+fx6mTtIE/3/7J6TjmIrhV6EWkk4hsFpFtIjIsh+3tRGSliKSJSM9s2/qLyFbXq39BBTfOOBl3hOZrPuK3WndTrYXdUuntrhhzJ/ulCulvjXY6irkIuRZ6EfEFJgCdgYZAbxFpmK3ZLuAeYEq2fcsDLwKtgVbAiyJS7uJjG6f8OWgipUgk/JXHnI5iikBw2QDWXPNPmh/8id0/rnM6jsknd87oWwHbVHWHqqYA04BuWRuoaqyqrgUysu3bEZinqkdU9SgwD+hUALmNA9JPJVHnx3f4veyNNOnTyOk4pog0fudBTlGKvUPfcjqKySd3Cn01YHeW5XjXOne4ta+IDBCRGBGJSUiwqcw81arBH1EpfR/JQ87pvTNerGqj8ixp8ACXb/qcQzGxTscx+eBOoc/pJml3L8G7ta+qTlLVaFWNjoiIcPPQpihpSipVPxvFyuAruWr4dU7HMUWs1rtPko4v2we87nQUkw/uFPp4oHqW5UjA3eeiL2Zf40HWPvkZVVN3ceSh5/HxtQekSpp611ZjUa37aLZqMic27nE6jskjdwr9cqCeiNQSkQCgFzDbzePPBTqISDnXRdgOrnWmGNHUNMpPeo0/A1pwzWt2iaWkqjb+aXxJZ9M/3nQ6ismjXAu9qqYBg8gs0BuB6aq6XkRGikhXABFpKSLxwO3ARBFZ79r3CPAymX8slgMjXetMMbLhpelUT9rGnnuexz/AzuZLqka3RLGgWl8a/TaJ0zsPOB3H5IGoetYTb9HR0RoTY5MTe4z0dGLLNiEx2Ydax9cQVMqesSvJYqZsofldDVh1/RNEz7f+ek8iIitUNTqnbfZbay5o0wtTiDq1gW29hluRN0T3qc/8ir25bME7JG63y23Fhf3mmvPSpGTKvP0Ca/1b0P79nrnvYEqEcuNH4kcqm+5+2ekoxk1W6M15bRgyiarJsewa+BrBpe1HxWRqeWdt/lvjQRov+w8nVmx1Oo5xg/32mhxlHD9JlQ9fZmnQdXR860an4xgPE/XhcJIIIq7fcKejGDdYoTc5Wv/AGMqnJXD8mVF2p405R+P2lZjb4DEab/iSI/NWOB3H5MIKvTlHavwBas18i5/L9KDD862cjmM8VNPPnuAQ4STc/zR42N175mxW6M05ttw2jICMJHzfeA0f+wkx51H38rLMa/Mil+yeT/w7Xzsdx1yA/Rqbsxz9cRmX/fEx30Q9xrUD6jsdx3i4G2Y8xDqfxvg/PRQ9ddrpOOY8rNCb/5eezol+g9hDVZp+9TxiXfMmFxWr+rFh4DtUSopj6wNvOB3HnIcVevO3XSMmU/PQChZ0epNLokOdjmOKiVvHXMOc0DupMe11kjfHOh3H5MAKvQFADx2mzKhnWep/NV2m9HY6jilGAgKg1IS3SFcfdvUYYhdmPZAVegPA9q5DKJ12jL3P/JuwctZnY/Lmur6RTL/0Rept+JaD785wOo7Jxgq94cin31N36ed8XuNZur/YxOk4ppi6/vuhrPCJJuCxR9CDNlOcJ7FCX8Lp0WPogw+yThrRds5zdjulybeadfzY8vRkSqUeY2fXwU7HMVnYr3UJF9vzCcKS9rNy0EfUbxTgdBxTzN35SmM+q/E8tX+fxtGPvnE6jnGxQl+CnZjyPbUWfMhnlZ6kz+gch7E2Jk98fOCqOc+wRprCQwPR/TZBiSdwq9CLSCcR2Swi20RkWA7bA0XkS9f230UkyrU+SkQSRWS16/V+wcY3+aW74+Ge/qyWZrSYPQI/P6cTGW9xSSN/Vj72OUHJx9lzQ1/IyHA6UomXa6EXEV9gAtAZaAj0FpGG2ZrdDxxV1brAGCDr1DPbVbWZ6zWwgHKbi5GWxt5reyOpKax99kuatApyOpHxMv3fbMT7l44jcsM8DjxuM1E5zZ0z+lbANlXdoaopwDSgW7Y23YBPXF/PAG4QsecqPdX+gSOotmMxE5tPpO/LNsyBKXg+PtB7wQN8E3gn4WOHk7xgidORSjR3Cn01YHeW5XjXuhzbuCYTPw6Eu7bVEpFVIvI/Ebk6pzcQkQEiEiMiMQkJdltWYUqc+QMVP/wX00rdxz0/9bFhDkyhqVxFCJkyiThqcqprL9i/3+lIJZY7hT6nUpD90bfztdkH1FDV5sBQYIqIlDmnoeokVY1W1eiIiAg3Ipn8yFi7Du3VizU0pdrM8VSo4HQi4+3a9yjD9/1nEHjqCAfadoekJKcjlUjuFPp4oHqW5Ugg+6zAf7cRET+gLHBEVZNV9TCAqq4AtgPWV+CEgwc51q4Lx9JCWPXSd1zdqbTTiUwJ8cgHzRnd7DMq7VjGwVvusyESHOBOoV8O1BORWiISAPQCZmdrMxvo7/q6J7BAVVVEIlwXcxGR2kA9YEfBRDduS07m4NU9CD6+n49v/ZZ7h0c6nciUIH5+8M+FPRhd4V9UnD+VI0NfcTpSiZNroXf1uQ8C5gIbgemqul5ERopIV1ezD4FwEdlGZhfNmVsw2wFrRWQNmRdpB6rqkYL+JswFpKZyqENvKm5ZwqiGn/LEly2tX94UubAw6LJkGFMD+lF+7AucGjPJ6UgliqiHfYyKjo7WmJgYp2N4h/R0jtzcl/Jzp/JyxHge3vhPwsNz382YwrLopxQSO3Wno/5I8qRPCX7gbqcjeQ0RWaGqOT75aE/GequMDI7e8SDl505lVNgo7llhRd4479oOAaR/OYNFXEfAgP4kf2EjXRYFK/TeKC2N43c8QLlZHzImZDi3xzxN9eq572ZMUbjl9mAOT/6WZVyBb9/epHwy1elIXs8Kvbc5fZpj1/eg7MzJvF1qOB2XvUSdOk6HMuZst98bwvbxP7BEryTgnj4kjhrndCSvZoXemxw5wrFWHSjz6/c8X34CXVaNpOFlduXVeKZ+/yzLvo/m8rV0J/iZIfz1z2fs1stCYoXeW6xZw8kGLQlev5wnqk/n4T8fpr49sWA8XK97gij1/Vd86PcgIf8exYkbe8Dx407H8jpW6L1A2sefkxLdhhMHk3is2SKGr+lJ1apOpzLGPR1v8qXx4vcYHjKGUvO/40SDVrB+vdOxvIoV+uLsxAlO9XkAv3v78ltaKyY9uJJxf7ShXDmngxmTN61aCw9tHsLgRgs5ve84yc1bk/buJOvKKSBW6Isp/Xk+f9VuTNDUybzt/zQHv/iZl96vhL+/08mMyZ+qVWHsiqv5970rWZzaGr9HHuRk206we3fuO5sLskJf3Bw4wKk+/0BubM+ew0E81HgxXdaN4o4+NnOIKf4CAuCVyVX5a9Y8hoVOwGfpYpLqNSLt7XGQmup0vGLLCn1xkZxM6qtvkFyzHgFTP2GM7xPMHbWa91a1sYuuxut06+7Dkzsf5rmuf/JLcmv8nhjCyVqN0Tk/OB2tWLJC7+kSE0kfP4G/qtbD//mnmZt8LUPar6fLpjcZ/HQwvr5OBzSmcISHw9hva5M+Zy4PRX7Hvj0ZyC03c6J5O/jpJ+u/zwMr9J4qIYHUV17nVOXa+D46iNVHajCwzjzKLJzNhHn1qVvX6YDGFI3ONwnv7LyFBePWMaz0O5xYvQM6duR4g9bol9MhJcXpiB7PCr0nSU+HRYs43uUu0qpE4j98GEtPXMYjDRdybPavvLe1Pdde63RIY4qenx8MHBzA8/sHMeuN7TxZdhKHNh9Get3J6fDqJA59FrZudTqmx7LRK52WlgbLlnFy8lfIzK8IObGPY5TlM+nPjhsfpNszDbnmGmxoYWOySE6GKZ+ls/qNn7h+6/vcwvf4ksHRqOaUuvdOAnv3gHr1nI5ZpC40eqUV+qKmClu2kLbwV45++RMhy+YRnHSMJAL5gZtYVuNOKv2jC3cPKEWlSk6HNcbzrV4NX43dg+/M6dz015dcwe8AHKtQh4wOnSjXsz1yVVvw8mlKrdA7RRV27yZj5WqOLVxF0tKVlFn3GyGJhwDYQ1XmSie21+tMuTs70PXuMnYHjTH5lJ4OS5bAgsmx6A8/cHnCf7mB+ZTmNABHwuuR3KINoe2aE3J1c2jSBG96uvCiC72IdALGAb7AB6o6Ktv2QOBT4HLgMHCnqsa6tj0D3A+kA4NVde6F3qvYFfqTJ2HvXtLj4jm+JpZT62NJ37qDgNjNlD+4iaC0UwBkIGyhPr9LG+JrtkWuakvDHpdy3fVC2bIOfw/GeKHYWJj/QzJ7v1tB4PLFXHp4MS1ZThX2/93meHAlTlS5lPQ69fGvX4vQJlGEXlYTiawGlStDYKBz30AeXVShd835ugW4kcxJwJcDvVV1Q5Y2DwNNVHWgiPQCuqvqnSLSEJgKtAKqAj8D9VU1/XzvV6iFXjXzz35qamYnX0oK6aeTSTmRROrJJFKPn858HTtF6rFTpB05Qfqxk2QcOw5HjyHHj+J7/AiBJxIo9VcCoUkHCU4/ddZbpOPDbqqzmUuID7mUk1UvRZs0pdw1TWjQMoSmTSEoqHC+PWPM+R06lNnNs23xfk4tXkXA5j8pd3AzUSmbqc8WKpJwzj4n/MtzKjiCxJAIUspGkF62HISVQ8qF4RNWBr9yoZmvsqXxDytNQFgp/MsE4x8ahH9oED7BgZlPgZ15+RTe/S8XW+jbACNUtaNr+RkAVX0tS5u5rjZLRcQP2A9E4Jo79kzbrO3O9375LfRHthwi6bLL8SEdH03HV9PxIR1fTcOPNPw0lQDy/2TdccpwlHIcoTzH/CI4GVSB06UrklS+KumVqiKR1QhuEEX5xtWoXtuf2rUhODjfb2eMKSJHjsCOHbB36ymOr40jeUsc7N2Lf8Jego/to/TpBMokJ1Au4xDlOEo5jlKKxHy9Vzo+pOJPuviRhh/p4keG+JKBL+niS3yly2kZ/02+jn2hQu/Oc/PVgKyDTcQDrc/XRlXTROQ4EO5avyzbvtVyCDgAGABQo0YNNyKdy690IFuqXYf6+KI+vmSIL+rrR4avH/j4kuHnj/r5o77+qH/mX1cNCIDAIAgKQoKDkFLB+ISWxifU9dc5PJTA8BCCK4ZStrwv5cpA9RDsISVjvEj58pkvoktD74ZAwxzbpaRk9tTuPw4nD6eQlHCS5EMnST1ykrTjp0g/eZqMk6fQpCRITILEREhJQVKSM1/paZCWiqSm4pORhqSn4ZOeimSk//1Kq16rUL5Hdwp9Tjf2Zf8YcL427uyLqk4CJkHmGb0bmc5Rploo18Z+nJ9djTEmVwEBmU/rhocDtQPIPJctHhMxu9NhFA9knXE0Eth7vjaurpuywBE39zXGGFOI3Cn0y4F6IlJLRAKAXsDsbG1mA/1dX/cEFmhm5/9soJeIBIpILaAe8EfBRDfGGOOOXLtuXH3ug4C5ZN5eOVlV14vISCBGVWcDHwKficg2Ms/ke7n2XS8i04ENQBrwyIXuuDHGGFPw7IEpY4zxAhe668YGNTPGGC9nhd4YY7ycFXpjjPFyVuiNMcbLedzFWBFJAOIu4hAVgEMFFKcgWa68sVx5Y7nyxhtz1VTVHMdi9rhCf7FEJOZ8V56dZLnyxnLljeXKm5KWy7pujDHGy1mhN8YYL+eNhX6S0wHOw3LljeXKG8uVNyUql9f10RtjjDmbN57RG2OMycIKvTHGeDmvLfQi8oSIqIhUcDrLGSLysoisFZHVIvKTiFT1gExvisgmV66vRSTM6UxniMjtIrJeRDJExNFb4USkk4hsFpFtIjLMySxZichkETkoIuuczpKViFQXkYUistH1//BRpzMBiEiQiPwhImtcuV5yOtMZIuIrIqtE5PuCPrZXFnoRqU7mZOa7nM6SzZuq2kRVmwHfAy84HQiYBzRS1SZkTgL/jMN5sloH9AB+cTKEiPgCE4DOZM4z19s18b0n+Bjo5HSIHKQBj6tqA+AK4BEP+TdLBq5X1aZAM6CTiFzhcKYzHgU2FsaBvbLQA2OAp8hh2kInqeqJLIul8YB8qvqTqqa5FpeROQuYR1DVjaq62ekcQCtgm6ruUNUUYBrQzeFMAKjqL2TOAeFRVHWfqq50fX2SzAJ2znzRRU0z/eVa9He9HP89FJFI4Gbgg8I4vtcVehHpCuxR1TVOZ8mJiLwqIruBu/CMM/qs7gN+dDqEB6oG7M6ynOMk9yZnIhIFNAd+dzZJJlcXyWrgIDBPVT0h11gyT04zCuPg7kwO7nFE5Gegcg6bngOeBToUbaL/d6Fsqvqtqj4HPCcizwCDgBedzuRq8xyZH7e/KOw8ec3mAdya5N6cS0RCgJnAkGyfaB3jmuWumet61Nci0khVHbvGISK3AAdVdYWIXFsY71EsC72qts9pvYg0BmoBa0QEMrshVopIK1Xd72S2HEwB5lAEhT63TCLSH7gFuEGL+MGKPPx7Ockmuc8HEfEns8h/oaqznM6TnaoeE5FFZF7jcPJidlugq4jcBAQBZUTkc1W9u6DewKu6blT1T1WtqKpRqhpF5i9oi6Iq8rkRkXpZFrsCm5zKcoaIdAKeBrqq6mmn83io5UA9EaklIgFkzok82+FMHk0yz7Q+BDaq6min85whIhFn7iwTkWCgPQ7/HqrqM6oa6apZvYAFBVnkwcsKfTEwSkTWichaMruXPOGWs38DocA8122f7zsd6AwR6S4i8UAbYI6IzHUih+ti9SBgLpkXFaer6nonsmQnIlOBpcAlIhIvIvc7ncmlLdAXuN71c7XadcbqtCrAQtfv4HIy++gL/HZGT2NDIBhjjJezM3pjjPFyVuiNMcbLWaE3xhgvZ4XeGGO8nBV6Y4zxclbojTHGy1mhN8YYL/d/DvrIaoZBJSEAAAAASUVORK5CYII=\n", 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EKkXklj5eFxG5PfL6GyIyL+q1/SLypohsE5GKeBbfl9ZAMDwXv/Xxe1Jaio9/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\nGI+x4DfGGI+x4DfGGI+x4DfGGI/5/4ZQoBGgyd13AAAAAElFTkSuQmCC\n", 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -426,7 +406,7 @@ "plt.plot(x, y)\n", "plt.axvline(2, color='r')\n", "data = gamma.rvs(2, size=1000)\n", - "plt.hist(data, bins=20, normed=True);" + "plt.hist(data, bins=20, density=True);" ] }, { @@ -440,9 +420,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -470,16 +448,14 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The mean of the means is: 4.00474211854\n", - "The standard deviation of the means is: 0.190485481767\n" + "The mean of the means is: 4.004742118538674\n", + "The standard deviation of the means is: 0.1904854817672302\n" ] }, { @@ -494,22 +470,26 @@ }, { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -522,7 +502,7 @@ "plt.boxplot(mean_of_data)\n", "plt.ylim(3, 5)\n", "plt.figure()\n", - "plt.hist(mean_of_data, normed=True)\n", + "plt.hist(mean_of_data, density=True)\n", "plt.xlim(3,5)" ] }, @@ -538,16 +518,14 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The mean of the means is: 4.00128131235\n", - "The standard deviation of the means is: 0.0654148250988\n" + "The mean of the means is: 4.001281312353626\n", + "The standard deviation of the means is: 0.0654148250988205\n" ] }, { @@ -562,22 +540,26 @@ }, { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -606,9 +588,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -649,17 +629,15 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "mean of the data: 38.1627400469\n", - "std of the data: 4.46032692087\n", - "std of the mean: 0.997359419693\n" + "mean of the data: 38.16274004693891\n", + "std of the data: 4.460326920868606\n", + "std of the mean: 0.9973594196934527\n" ] } ], @@ -677,18 +655,18 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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N9UM7B9XFWv7q3qEFEwclMWd5HgcLTzkdx4QYK/wmKD32yTaiIoU7zw+9vf3T7vleTypc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\nAElFTkSuQmCC\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -716,25 +694,25 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "sample mean, standard deviation of sample mean: 69.37100000000001 3.50960830773\n" + "sample mean, standard deviation of sample mean: 69.37100000000001 3.5096083077295406\n" ] }, { "data": { - "image/png": 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hUZbbocSc5bPzyM1I5uVD9Vy3bKbb4ZgoZ0fumqjQ6/OzqaqR9yyahoidpmGs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\n2X5/8Fd2yC9tnz+AL6D0+xV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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -752,31 +730,31 @@ "plt.axvline(perc025, color='r')\n", "plt.axvline(perc975, color='r')\n", "plt.axvline(mu20, color='k', lw=4)\n", - "plt.title('H0 is rejected: mean is not 50 Pa');" + "plt.title('H0 is rejected: mean is not 50 N/mm2');" ] }, { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "sample mean, standard deviation of sample mean: 48.65050561797753 0.903543631702\n" + "sample mean, standard deviation of sample mean: 48.65050561797753 0.9035436317023355\n" ] }, { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -795,7 +773,7 @@ "plt.axvline(perc025, color='r')\n", "plt.axvline(perc975, color='r')\n", "plt.axvline(mu, color='k', lw=4)\n", - "plt.title('Not enough evidence to reject H0: mean may very well be 50');" + "plt.title('Not enough evidence to reject H0: mean may very well be 50 N/mm2');" ] }, { @@ -810,38 +788,42 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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yl7TQeecndN2xNf2GZMrl58Pbb+sGHpIyKndJC+U35ljYpbfnJLWUnx9M//vqq76TSD2h\ncpe08PXiZXxwwkl81Kq97yi1c/75cMIJGneXlFG5S/jFbswRunul1kRmZnADj7lzdQMPSQmVu4Tf\nq6+SVVYa3htzxCs/H7Zvh2XLfCeRekDlLuE3axY7GzYL74054jV0aHAGX1joO4nUA5o4TMKttBTm\nzAn3jTkqqWpCsc0P5kOrVsENPAoL4Wc/85BM6hOduUu4LV0Kn3+e3uPtFRUUwNq1sGmT7yQScSp3\nCbdZs6BBAxaf3Mt3ksQoKAj+1NCMJJnKXcKtsBAGDGB3w6a+kyRG167QvTvMnOk7iUScyl3Ca/Pm\nYAhj+HDfSRKroAAWL4YvvvCdRCJM5S7hVT50MWKE3xyJVlAAR4/CvHm+k0iEqdwlvKZNC4YwunXz\nnSSx+vSB3FwNzUhSqdwlnLZvD4YuRo3ynSTxMjODoaY5c+DIEd9pJKJU7hJOhYXBNe6XX+47SXIU\nFMCuXfDmm76TSESp3CWcpk2Dk06Cc8/1nSQ5LrkkuAXftGm+k0hEqdwlfPbtg9deC87a0+VeqTXV\npAkMGwavvAJlZb7TSASp3CV85s2DgwejOd5e0ejR8MknwadwRRJM5S7hM306tG4dzMMSZSNGQFYW\nvPyy7yQSQZo4TLypcoKtB4YEUw6MHAkNIv7j2aJFMPY+dSr88pfRHYISL3TmLuGyaFHwyc2oD8mU\nGz0aPvwQVq70nUQiRuUu4fKnPwVvNl5yie8kqTFyZHDdu4ZmJMHiKnczG2ZmfzOzYjObUMXygWa2\ny8xWxR73Jj6qRF1W6ZGg5EaOhMaNfcdJjRNOgIsvDoZmnPOdRiKk2nI3s0zgceBSoDtwlZl1r2LV\nN51zZ8ce9yc4p9QD/Tevgh074MorfUdJrdGj4YMPgknSRBIknjP3PkCxc26Tc+4w8AdgZHJjSX00\nYsNiaNkShgzxHSW1Ro0K3kydOtV3EomQeMq9A/Bxha+3xF6r7AIzW21mc82sR1UbMrNxZlZkZkUl\nJSW1iCtRlXPkEEM+eCc4i83J8R0ntdq1C4Zmfv97Dc1IwiTqDdWVQCfn3JnAo8D0qlZyzj3tnOvt\nnOudm5uboF1LFAzctIJmhw/UvyGZctdeC8XFsGyZ7yQSEfGU+1bgpApfd4y99k/Oud3Oub2x53OA\nLDNrk7CUEnkjNiympHFLGDjQdxQ/vvGNYK6ZF17wnUQiIp5yXw50M7OTzSwbuBL4l4mozexEs+AT\nGGbWJ7bd7YkOK9HU5NB+vr5xOXNO7x/9Dy4dS4sWwUyRf/iDpgGWhKi23J1zR4FbgVeBDcBLzrl1\nZjbezMbHVhsDrDWz94BHgCud0+ChxGdw8VIaHT1E4Rlf8x3Fr2uvhc8/DyZNE6mjuE6TYkMtcyq9\n9mSF548BjyU2mtQXo9cuYEvzXFZ0OMN3FL+GDg2ue3/hBcjP951G0pw+oSpeddj1GRduXsXUnoNx\nVs9/HLOz4VvfCiZO273bdxpJc/V0gFNSqaoJwsp9c83rAPyp5yXVrpvOqpwk7cEqzs6vuQaeeCK4\nicd116UgmURVPT9VEp8yykoZs+YN3so7m60t2vqOEw79+kHXrvDcc76TSJpTuYs3F3y4mo67S3jp\nzHoySVg8zODmm4Obg2/Y4DuNpDGVu3jzrdWvsbNhM17r1s93lHC54YbgJh5PPeU7iaQxlbt40fLA\nboZ8sITpPQZyuEGW7zjh0rYtjBkDv/0t7N/vO42kKZW7eHH5ukXklB7VkMyxjB8f3LTkpZd8J5E0\npXKXlDNXxtiVs1jV/lQ2tO3iO044DRgAZ5wBkyb5TiJpSuUuKTdo43K67PyEyedd7jtKeJkFZ+/L\nlukWfFIrKndJue8sn86W5rnMPa2/7yjhNnYsNGqks3epFZW7pFSPfxTT76M1/ObcEZRmZPqOE24t\nWwYFP2UKbNvmO42kGZW7pNRNRTPYm92IP5411HeU9PBv/wZHj8LDD/tOImlG5S4p027P54zYsJg/\nnjmEPTlNfMdJD127BvPNTJoEO3f6TiNpROUuKXPDikIynOP5c0f4jpJeJkyAvXvhMU28KvHTxGGS\nEm327WTsylnMOn0AW1qe6DtOKNRkkrTJXc+j189/Rf+d3TmQ3fC461Y5IZnUOzpzl5T43pKXyD56\nhIcvvNp3lLT0RN8raH1gN1e996rvKJImVO6SdB13fco1787lpTMvYXPrDr7jpKWVHc9gSaee3LL0\nTzQ5pCkJpHoqd0m6O976Pc6MRy64yneUtPbgRdeTu+8Lblk61XcUSQMqd0mu9esZtW4hv+01nH80\nb+M7TVp77yunMa37QG5eNo0Ouz7zHUdCTuUuyeMcTJjA/qwcJvUd4ztNJDx00XWUWQb3/Pk3vqNI\nyKncJXleeQUKC3n0givZ2biF7zSRsK15Lk/3GUXBhsX02qqbecixqdwlOXbuhFtvhXPO4VlNEJZQ\nT50/mk+btmbiG0+TWVbqO46ElMpdkuOee6CkBCZP1hwyCbY/uxE/+fo4zvrHB3x3ieZ7l6qp3CXx\n/vxneOYZuOsu6NXLd5pImnP6hUzrPpDb3v4DPbd94DuOhJDKXRJrx47gHqBdusB99/lOE2kTLxlP\nSZNWPDzrP8k5csh3HAkZlbskTmkpXHMNbNkCL7wAjRv7ThRpuxs25QeX3cEpO7bw74ue9x1HQkbl\nLokzcSLMmwePPgr9+vlOUy+8nXc2k3uP5PqVs7hy1TzfcSRENHGYJMb06fAf/wE33QTjxvlOU6/8\n/OIbOWX7Fn762hNsa55L3oS6bU8Tj0WDztyl7hYsgKuvhvPOC6alNfOdqF4pzcjkeyPv4f3czjw+\n40G6f7rJdyQJAZW71M0bb0B+fvAG6qxZ0PD409FKcuzLacwNYyayO6cJz0+9j9NKNvuOJJ6p3KX2\nXn8dRoyAbt1g4UJo29Z3onrt02ZtuO6bP8EBf/rdPZz/0RrfkcQjlbvUnHPBm6b5+XDqqTB/PuTm\n+k4lwAe5nRl97a/4rEkrprz0Yy7961u+I4knKnepmd27g3t63nYbDB0Kixap2ENma4u2jLn2Idac\n2I1JMx5k4htP0fDIQd+xJMVU7hIf52D27OATp6+8Ar/4BcyYAa1a+U4mVfiiUXOu+dZPee7cAm5Y\nUcic52/TRGP1jMpdqrd+PQwbBsOHQ2ZmML5+992QoR+fMDuUlcP9g8dx1ZU/I7v0KFNfuJtfz/wl\nXbd/7DuapIB+O6VqZWXBB5JGjICvfhWWLYOHH4Y1a2DAAN/ppAaWdD6TYTc+xpN9RzO4eCmvPfs9\nHi78Fb22bAj+RyaRpA8xyf8pLYUlS6CwEF5+GTZuhHbt4Ec/gttvhza6k1K62pvTmIcuup7J541i\n3NKXGfvubEatX8TfW7VnWo9BzO96HuvbdcGZzveiwlwc/3Kb2TDg10AmMNk592Cl5RZbfhmwH7je\nObfyeNvs3bu3Kyoqqm1uqauyMti6NRhyWbYseLz9djDxV4MGMHAg3HgjjB4N2dl12lXehNmJySwJ\n0+TQfi59/22+sXYBF3y0GoCdDZuxpFNPLrtpJJx1VvBo104fSgsZM1vhnOtd3XrVnrmbWSbwOHAJ\nsAVYbmYznXPrK6x2KdAt9jgfmBT7UxLJuaCUS0uDx9GjwePwYTh0KHgcPAj79gWPvXuDm2bs3Anb\nt8O2bfDJJ8HEXsXFwboQ/PKefjqMHBmMrQ8dCi1056Qo25fTmKk9BzO152By9+6g/4fv0X/ze/T7\naHUwF3+5pk0hLy94tG8ffJahbdvgjfTmzYNHkybQqFHwaNgQsrKCE4KsrOA9mgYNgj/N9A9FCsUz\nLNMHKHbObQIwsz8AI4GK5T4SmOKC/wa8Y2Ytzay9c25bwhNPmwZjxyZ8s0lzrP8ZVXy9/LlzX35e\n/igrq9v4aEZGcBbWvj2cfDIMGRJco37qqcEVMCrzequkaWum97iY6T0uBmDz3f1g9ergsWkTbN4c\nPJYvD27AUlZW+52ZBT+L5UVf8VG+vOK6VT2vvL10dOedcP/9Sd1FtcMyZjYGGOac+07s628D5zvn\nbq2wzizgQefcW7Gv5wP3OOeKKm1rHFA+q9RpwN9qmbsN8HktvzeZwpoLwptNuWpGuWomirk6O+eq\n/XBJSt9Qdc49DTxd1+2YWVE8Y06pFtZcEN5sylUzylUz9TlXPG+NbwVOqvB1x9hrNV1HRERSJJ5y\nXw50M7OTzSwbuBKYWWmdmcBYC/QFdiVlvF1EROJS7bCMc+6omd0KvEpwKeRzzrl1ZjY+tvxJYA7B\nZZDFBJdC3pC8yEAChnaSJKy5ILzZlKtmlKtm6m2uuK5zFxGR9KKPo4mIRJDKXUQkgkJd7mY2zMz+\nZmbFZval2/7G3sB9JLZ8tZn1CkmugWa2y8xWxR73pijXc2b2mZmtPcZyX8erulwpP15mdpKZLTSz\n9Wa2zsxur2KdlB+vOHP5OF4NzWyZmb0Xy/WTKtbxcbziyeXl9zG270wzezf2WaDKy5J7vJxzoXwQ\nvHm7EegCZAPvAd0rrXMZMBcwoC+wNCS5BgKzPByzrwG9gLXHWJ7y4xVnrpQfL6A90Cv2vBnwfkh+\nvuLJ5eN4GdA09jwLWAr0DcHxiieXl9/H2L7vAn5f1f6TfbzCfOb+z2kPnHOHgfJpDyr657QHzrl3\ngJZm1j4Eubxwzi0GdhxnFR/HK55cKeec2+Zik9s55/YAG4AOlVZL+fGKM1fKxY7B3tiXWbFH5asx\nfByveHJ5YWYdgXxg8jFWSerxCnO5dwAq3lVgC1/+IY9nHR+5AC6I/Vdrrpn1SHKmePk4XvHydrzM\nLA84h+CsryKvx+s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\n", 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\n", 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Mvwf4HjAIuNuCk1XqnHNF8YstoXXnnZCZCbfe6jtJ2mi8Zj7O8czgkZz8/k+4\nunz46enbfjjfUzLxKaqbdTjnlgBLmr13T5Ofvwh8MbbRJOkcOAC//S1cdx0MHeo7Tfox4/cz5vKD\n539FUUUpxXlTfCcSj3SGqsTOPffAiRPwta/5TpK2/jj9Mvb36MuXX9cZq+lO5S6xUVMDv/gFXH45\nTJvmO03aOpnZnd/MupqPbSlm8p4tvuOIRyp3iY1Fi2D3bvjGN3wnSXu/K7iSI1k9+fIb2npPZyp3\n6bz6eviv/wq22HX4o3dHuvfmfwvnc+XGVxl94ENHLUuaULlL5z30EJSWwne/q0v7hsT9RQs41TWT\nhW/8yXcU8UTlLp1z4kRQ6ueeC5/6lO80ErG/V38WnX0Ff1v6Amze7DuOeBDVoZAiLRl1+2JuXfEI\n36yq4pMX30bxPy1p+0OSMHef92k+ufZ5Mm+7LbgkhP5XlVa05S4dNvj4QRa++WeemfARHVMdQtW9\nB3Dn7M/C0qW6U1MaUrlLh9322iK61Z3iRxd9zncUacWDM6+CSZPgq1+Fkyd9x5EEUrlLx6xYwWdL\nnuHhGfPYqjsthVZdRtfg/IMtW+CnP/UdRxJI5S7td+wY3HADVX2z+a8Lb/SdRtpyySXwd38H//7v\n2rmaRrRDVdrvG9+ArVv5+rX/ybFuPX2nkTaMun0xQ4dcxVL3LJUXzOPvrv8JNV2zPjCPLi6WerTl\nLu3z9NNw773wj//IyrOm+k4jUdrddzDfnP9Vpu4p559f/I3vOJIAKneJ3vbtcPPNMH06/OAHvtNI\nO/113Ln8etbV3LR6MfM2vuo7jsSZyl2ic+AAzJv3/v1Ru3XznUg64McX3UTJsAn8aOnPGbdvh+84\nEkcqd2nbyZOwYAGUl8MTT8Dkyb4TSQfVZmRy64JvU5OZxcN/+C4jD1b5jiRxonKXM6utheuvh1df\nDa4hc9FFvhNJJ1X0y+G6z/w/MuvrePiR7zD8yF7fkSQOdLSMtO7IEfj0p2HZMvjZz+Azn/GdSGLk\n3exR3PDpH7Doke/w+0XfYbaDyn5DzvgZHVGTXLTlLi3buRPOPx/++le47z7dXSkFlQ4dx02f+jcG\nvneEpx78KrO3lfiOJDGkcpcPW7YsuMrj9u3BdUluvtl3IomTNbkT+Zsbf0Z1r/489Mfvccubfwbn\nfMeSGNCwjLzvwIFgC/2hhygbmMeXP/kfvPt8DTy/2HcyiaNtA3P5xA0/5cdL7uKfX/otl5Sv5PuX\n/h82Dhl+7PBoAAAGvElEQVTtO5p0gspd4PjxYOjlP/4jKPjvfpcr35vJqa6ZvpNJgpzI6sGtC77N\nq2/P4FvLH2LxA7fxUOF8fvHRazjQs5/veNIBGpZJZ7t3B4U+alRw1cBJk6C4GO64Q8Wejsx4ZMZc\nLv7Sr3h4xjxuXL2Y1+/+HD9achcT9271nU7ayZyn8bWioiJXXFzsZdlprbISnnkmOBHpxRehoSE4\nOek734HZs0/PNup2DcWku7H7d/L54if529IX6Flbw9qcsTw7/jyenfARNg0e2eLNP3RETfyZ2Srn\nXFGb80VT7mY2F7gLyADuc879sNl0i0y/EjgBfM45t/pM36lyT4CDB2HduuBRXAwvvxyciAQwbhxc\ne23wmDTpQx9VuUujvieP8al3nuPKTa8xs2ojANW9+rN6+ERW506kdMhYygflsbvPILb+6CrPaVNf\nzMrdzDKAd4HLgApgJXCtc259k3muBP4vQbmfC9zlnDv3TN+rcm/GueDR0AD19VBXFzzX1gaPU6eC\nM0VPnoT33gvGyY8cCR4HD8K+fcFj1y7YsSM40uXAgfe/f+BAuOCC4CSkiy+Gs88+423XVO7Skuxj\nB7ik7C3OqSiloGojow/uOj3tRGY3dvbLYU/vQezuM4jqXgM41L0Ph3r05ki33pzI6s7xzB489s1L\noXv34NGtG2RmBo+uXSEj4/1HF40atySW5f4R4F+dc1dEXv8TgHPuP5vM8yvgJefcosjrTcAc59yu\nFr4S6ES5/+UvcGNIriHe2rpr+n7zn1t6NDR0/vCzLl1g0CDIyYGRI2HECBg9GqZODR55eafLXMUt\nsTLwxGEm7NvOmAOVjN1fQd7hPeQc20/O0QMMPnGIzIb6zi3ALPjdNvvgo3Fa0w2U1n5u/n1h8LWv\ndfjie9GWezRHy+QCO5u8riDYOm9rnlzgA+VuZrcAt0ReHov8I9ARg4F9HfxsPPnL1dAA1dXBY926\nlubQOmsf5YrCdmBN8GN8cjkX/A+240K1vk67447B3HFHR3ONjGamhB4K6Zy7F7i3s99jZsXR/MuV\naGHNBeHNplzto1ztk865ohnUqgTOavI6L/Jee+cREZEEiabcVwLjzWy0mWUB1wBPNpvnSeBGC5wH\nHD7TeLuIiMRXm8Myzrk6M7sVWEZwKOT9zrlSM1sYmX4PsITgSJkygkMhPx+/yEAMhnbiJKy5ILzZ\nlKt9lKt90jaXt5OYREQkfnQgqYhIClK5i4ikoFCXu5nNNbNNZlZmZre3MN3M7OeR6e+YWWFIcs0x\ns8NmVhJ5fC9Bue43s71m1uKB7h7XV1u5Er6+zOwsM3vRzNabWamZ3dbCPAlfX1Hm8rG+upvZW2b2\ndiTXv7Uwj4/1FU0uL38fI8vOMLM1ZvZ0C9Piu76cc6F8EOy8LQfGAFnA28DkZvNcCSwFDDgPeDMk\nueYAT3tYZxcChcC6VqYnfH1FmSvh6wsYBhRGfu5DcImNMPx+RZPLx/oyoHfk50zgTeC8EKyvaHJ5\n+fsYWfbXgd+3tPx4r68wb7mfA5Q557Y4504BjwALms2zAHjIBd4A+pvZsBDk8sI5txw4cIZZfKyv\naHIlnHNul4tc3M45dxTYQHBWdVMJX19R5kq4yDo4FnmZGXk0PxrDx/qKJpcXZpYHzAfua2WWuK6v\nMJd7a5c0aO88PnIBfDTyX62lZjYlzpmi5WN9Rcvb+jKzUUABwVZfU17X1xlygYf1FRliKAH2As85\n50KxvqLIBX5+v+4EvgU0tDI9rusrzOWezFYDI5xz04FfAI97zhN23taXmfUG/gx81Tl3JFHLbUsb\nubysL+dcvXNuBsEZ6OeY2dRELLctUeRK+Poys48De51zq+K9rNaEudzDetmDNpfpnDvS+F9F59wS\nINPMBsc5VzRCeZkIX+vLzDIJCvRh59xjLcziZX21lcv375dz7hDwIjC32SSvv1+t5fK0vmYDf2Nm\n2wiGbj9mZr9rNk9c11eYyz2slz1oM5eZDTULri1qZucQrOf9cc4VjVBeJsLH+oos7zfABufcz1qZ\nLeHrK5pcntZXtpn1j/zcg+D+DhubzeZjfbWZy8f6cs79k3Muzzk3iqAjXnDOXd9striur9DeINuF\n87IH0eb6JPD3ZlYHvAdc4yK7x+PJzBYRHBkw2MwqgO8T7GDytr6izOVjfc0GbgDWRsZrAf4ZGNEk\nl4/1FU0uH+trGPCgBTfv6QL80Tn3tO+/j1Hm8vL3sSWJXF+6/ICISAoK87CMiIh0kMpdRCQFqdxF\nRFKQyl1EJAWp3EVEUpDKXUQkBancRURS0P8HYJKwWJilI2MAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -853,7 +835,7 @@ " mu = np.mean(mean_of_data)\n", " sig = np.std(mean_of_data, ddof=1)\n", " plt.figure()\n", - " plt.hist(mean_of_data, bins=20, normed=True)\n", + " plt.hist(mean_of_data, bins=20, density=True)\n", " x = np.linspace(0, 4, 100)\n", " y = norm.pdf(x, loc=mu, scale=sig)\n", " plt.plot(x, y, 'r')\n", @@ -884,9 +866,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.7.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebook12_oop/py_exploratory_comp_12_sol.ipynb b/notebook12_oop/py_exploratory_comp_12_sol.ipynb index fe1a1ae..bd2ec93 100644 --- a/notebook12_oop/py_exploratory_comp_12_sol.ipynb +++ b/notebook12_oop/py_exploratory_comp_12_sol.ipynb @@ -23,14 +23,12 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ + "%matplotlib inline\n", "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" + "import matplotlib.pyplot as plt" ] }, { @@ -38,9 +36,9 @@ "metadata": {}, "source": [ "### A Triangle Class\n", - "So far, we have learned what is called *functional* programming. In functional programming you write or use functions that manipulate data. For example, consider the case where we have to deal with a number of triangles. For each triangle we want to be able to compute its area, and we want to be able to plot it, and fill the inside with a color. Say we have an arbitrary number of $N$ triangles. For each triangle we need to store the $(x,y)$ values of its three corner points. So we create arrays for the $x$ values of each corner point, we create arrays for the $y$ values of each corner point. Then we write a function that computes the area of a triangle given its three corners, and we write a function that plots the triangle given the three corner points and color to fill the triangle, and finally we need to loop through all the corner points. This all sounds like a bit of work, but it is tracktable. It already gets more complicated when we want to change the corner point of one triangle. We have to know its place in the array, and change the correct corner point.\n", + "So far, we have learned what is called *functional* programming. In functional programming you write or use functions that manipulate data. For example, consider the case where we have to deal with a number of triangles. For each triangle we want to be able to compute its area, and we want to be able to plot it, and fill the inside with a color. Say we have an arbitrary number of $N$ triangles. For each triangle we need to store the $(x,y)$ values of all three corner points. So we create an array for the $x$ values of the three corner point, we create an array for the three $y$ values of each corner point. Then we write a function that computes the area of a triangle given its three corners, and we write a function that plots the triangle given the three corner points, maybe fill each triangle with a color. And finally, we need to loop through all the triangles. This all sounds like a bit of work, but it is tractable. It gets more complicated when we want to change the corner point of one triangle. We have to know its place in the array, and change the correct corner point.\n", "\n", - "It gets even more complicated when we have to deal with both triangles and rectangles. Triangles have three corner points, while rectangles have four corner points. The function to compute the area of a rectangle is very different, hence we have to make sure we call the area function for a triangle when we have a triangle, and the area function for a rectangle when we have a rectangle. The plotting is not much different, but we have to supply it four corner points rather than three. This gets a bit messier already. Wouldn't it be nice if it was possible to organize the data and functions in such a way that the data itself knows how to compute its area or how to plot itself? That may sound magical, but that is exactly what Object Oriented Programming does. \n", + "It gets even more complicated when we have to deal with both triangles and rectangles. Triangles have three corner points, while rectangles have four corner points. The function to compute the area of a rectangle very different, hence we have to make sure we call the area function for a triangle when we have a triangle, and the area function for a rectangle when we have a rectangle. The plotting is not much different, but we have to supply it four corner points rather than three. This gets a bit messier already. Wouldn't it be nice if it was possible to organize the data and functions in such a way that the data itself knows how to compute its area or how to plot itself? That may sound magical, but that is exactly what Object Oriented Programming does. \n", "\n", "Object oriented programming is, in essence, just another way of organizing your data and functions. Rather than defining and storing them separately, the data and functions are stored and bound together in what is called a *Class*. The data that are stored are called *attributes*, and the functions are called *methods*. \n", "This is probably easiest understood by writing a class and using it. Consider, for example, the class `Triangle` that stores the coordinates of the three corner points. Don't worry about the syntax yet (we will get back to that). Run the code below so we can start using the class. " @@ -49,9 +47,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "class Triangle:\n", @@ -75,9 +71,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "t1 = Triangle((0, 1), (3, 0), (2, 3))" @@ -97,15 +91,13 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "<__main__.Triangle object at 0x1101b37b8>\n", + "<__main__.Triangle object at 0x11a0618d0>\n", "(0, 1)\n", "(3, 0)\n", "(2, 3)\n" @@ -125,21 +117,19 @@ "source": [ "Let's get back to the `Triangle` class. When we call the `Triangle` class (official lingo: we create a `Triangle` object, or more officially yet: we create an instance of the `Triangle` class), Python calls the `__init__` function. This function is called the *constructor*. It constructs an object. In the constructor you define what arguments need to be provided to create a triangle. The name `__init__` (that is *two* underscores before and after the word `init`) is required (it is one of the few unfortunate name choices of the Python language). The first argument is `self` and tells Python what the object itself is called inside the class. \n", "\n", - "We saw above that typing `print t1` returns a meaningless message. This can be resolved by including a representation function, which needs to be called `__repr__`. This function is called when the object is printed (or converted to a string)." + "We saw above that typing `print(t1)` returns a meaningless message. This can be resolved by including a representation function, which needs to be called `__repr__`. This function is called when the object is printed (or converted to a string)." ] }, { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Triangle with corners:(0, 1)(3, 0)(2, 3)\n" + "Triangle with corners: (0, 1), (3, 0), (2, 3)\n" ] } ], @@ -150,7 +140,8 @@ " self.x1y1 = x1y1\n", " self.x2y2 = x2y2\n", " def __repr__(self):\n", - " return 'Triangle with corners:' + str(self.x0y0) + str(self.x1y1) + str(self.x2y2)\n", + " return f'Triangle with corners: {self.x0y0}, {self.x1y1}, {self.x2y2}'\n", + " \n", "t1 = Triangle((0, 1), (3, 0), (2, 3))\n", "print(t1)" ] @@ -169,9 +160,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "class Triangle:\n", @@ -182,7 +171,7 @@ " self.x = np.array([self.x0y0[0], self.x1y1[0], self.x2y2[0]])\n", " self.y = np.array([self.x0y0[1], self.x1y1[1], self.x2y2[1]])\n", " def __repr__(self):\n", - " return 'Triangle with corners:' + str(self.x0y0) + str(self.x1y1) + str(self.x2y2)\n", + " return f'Triangle with corners: {self.x0y0}, {self.x1y1}, {self.x2y2}'\n", " def area(self):\n", " A = 0.5 * np.abs((self.x[0] - self.x[2]) * (self.y[1] - self.y[0]) - \n", " (self.x[0] - self.x[1]) * (self.y[2] - self.y[0]))\n", @@ -193,15 +182,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that the `area` function gets passed the object `self`; once it knows what `self` is, it has access to all its attributes and functions. We can now create a `Triangle` object and compute its area as follows (don't forget to run the new `Triangle` class above first)" + "Note that the `area` function gets passed the object `self`. Once it knows what `self` is, it has access to all its attributes and functions. We can now create a `Triangle` object and compute its area as follows (don't forget to run the new `Triangle` class above first)" ] }, { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -226,9 +213,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -252,9 +237,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -312,9 +295,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "class Triangle:\n", @@ -326,7 +307,7 @@ " self.y = np.array([self.x0y0[1], self.x1y1[1], self.x2y2[1]])\n", " self.color = color\n", " def __repr__(self):\n", - " return 'Triangle with corners:' + str(self.x0y0) + str(self.x1y1) + str(self.x2y2)\n", + " return f'Triangle with corners: {self.x0y0}, {self.x1y1}, {self.x2y2}'\n", " def area(self):\n", " A = 0.5 * np.abs((self.x[0]-self.x[2])*(self.y[1]-self.y[0]) - \n", " (self.x[0]-self.x[1])*(self.y[2]-self.y[0]))\n", @@ -339,7 +320,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's create three triangles and store them in a list. Then we loop through the triangles in the list and plot them in one graph. Note how we can loop through the triangles in the list `tlist`:\n", + "Let's create three triangles and store them in a list. Then we loop through the triangles in the list and plot them in one graph. Note how we can loop through the triangles in the list `tlist` as follows:\n", "\n", "`for t in tlist:`\n", "\n", @@ -349,18 +330,18 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -388,9 +369,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -404,7 +383,7 @@ "areatot = 0.0\n", "for t in tlist:\n", " areatot += t.area()\n", - "print('total area:', areatot)" + "print(f'total area: {areatot}')" ] }, { @@ -418,9 +397,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -456,16 +433,13 @@ "* $(x_w,y_w)=(-20,0)$, $Q=-50$ m$^3$/d\n", "* $(x_w,y_w)=(20,0)$, $Q=-50$ m$^3$/d\n", "\n", - "When your implementation is correct, the head caused by the three wells at $(x,y)=(20,5)$ is 0.2968 m. Warning: don't fall in the trap of integer division (remember `1/2 = 0`, while `1.0 / 2 = 0.5`).\n", - "Plot the variation of the head along the line $y=1$ for $x$ varying from -40 to +40." + "When your implementation is correct, the head caused by the three wells at $(x,y)=(20,5)$ is 0.2968 m. Plot the variation of the head along the line $y=1$ for $x$ varying from -40 to +40." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -487,9 +461,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -514,9 +486,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -533,10 +503,10 @@ } ], "source": [ - "print('number of dimensions of x:', x.ndim)\n", - "print('shape of x:', x.shape)\n", + "print(f'number of dimensions of x: {x.ndim}')\n", + "print(f'shape of x: {x.shape}')\n", "x.shape = (4, 3)\n", - "print('new shape of x:', x.shape)\n", + "print(f'new shape of x: {x.shape}')\n", "print(x)" ] }, @@ -550,22 +520,20 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "mean of x: 5.5\n", - "max of x: 11\n" + "mean of x: 5.5\n", + "max of x: 11\n" ] } ], "source": [ - "print('mean of x: ', x.mean())\n", - "print('max of x: ', x.max())" + "print(f'mean of x: {x.mean()}')\n", + "print(f'max of x: {x.max()}')" ] }, { @@ -573,26 +541,26 @@ "metadata": {}, "source": [ "### Plotting features are objects\n", - "All plotting commands we have used so far are functions that are part of the `matplotlib` package. Not surpringly, `matplotlib` has an object-oriented design. Plots may be created by making use of the object-oriented structure. This requires a bit of additional typing, but in the end, we gain additional flexibility and the ability to make animations.\n", + "All plotting commands we have used so far are functions that are part of the `matplotlib` package. Not surpringly, `matplotlib` has an object-oriented design. Plots may be created by making use of the object-oriented structure. This requires a bit of additional typing, but in the end, we gain a lot of additional flexibility.\n", "\n", - "Using the OO syntax, we first create a `figure` object and specify the size using the `figsize` keyword argument (the size of the figure is specified in inches), then we add an axis to the figure with the `add_axes` command (note that it is `axes` with an `e`) by specifying the *relative* location of the axis in the figure. The location of the left, bottom, width, and height are specified in relative coordinates (both the horizontal and vertical direction run from 0 to 1). To plot, we use the `plot` method of the axis." + "Using the OO syntax, we first create a `figure` object and specify the size using the `figsize` keyword argument (the size of the figure is specified in inches), then we add an axis to the figure with the `add_axes` command (note that it is `axes` with an `e`) by specifying the *relative* location of the axis in the figure. The location of the left, bottom, width, and height are specified in relative coordinates (both the horizontal and vertical directions run from 0 to 1). To plot, we use the `plot` method of the axis." ] }, { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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MybNFHH44rLZavuR6112GOqkWGewkqUJNnpxnjdh++9w54oor4IUXoFu3oiuT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\nSVLlqojOExHRC+gF0KFDh7J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\n", 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wE5ELK1YMBg92I3inC7yaNd0yuT//9DqdiFyCU6fczoNPP+1mXfz4I6j5hH9T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\n4PX7pzdDZGSkb8L4sWPHoH9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+ "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -641,8 +609,10 @@ "ax1.set_xlabel('x')\n", "ax1.set_ylabel('y')\n", "ax1.set_title('Example figure')\n", - "ax2 = fig.add_axes([0.15, 0.5, 0.4, 0.3])\n", + "ax2 = fig.add_axes([0.18, 0.5, 0.4, 0.3])\n", "ax2.plot(x,-y,'r')\n", + "ax2.set_xlabel('x-axis')\n", + "ax2.set_ylabel('vertical value')\n", "ax2.set_title('Second axis');" ] }, @@ -650,34 +620,110 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Matplotlib patches\n", - "The plotting package `matplotlib` includes a set of classes to define shapes, which are called *patches* in `matplotlib`. There are patches for many different shapes including circles, ellipses, polygons, wedges, and arrows. Here we learn how to draw these patches. We learn how to make them move interactively in a future notebook.\n", - "\n", - "The process for adding a patch to a graph is always the same. First you create an axis, then you create a patch object and you add the patch object to the axis. Each patch object has a few input arguments and a number of keyword arguments. The keyword arguments include: `ec` for edge color, `fc` for face color, `alpha` for transparancy, and `zorder` for the order in which they are plotted (the patch with the highest `zorder` value lies on top). The names of all patch classes start with a capital: `Circle`, `Ellipse`, `Polygon`, `Wedge`, `Arrow`. You need to import these classes from `matplotlib.patches` to be able to use them. Use the help system to learn about the required input arguments. The graph below contains two circles, where the smaller one is on top of the larger one. The face color of the graph is set to the same color as the small circle, so that it looks like the large circle has a hole. The aspect ratio of the axis is set to `'equal'` when the axis is created. The `autoscale` function needs to be called to set the limits of the axis such that the patches fit exactly in the axis. Alternatively, you can call the `ax.set_xlim` and `ax.set_ylim` functions to select limits of your own choice." + "Another way to add an axis to a figure is using the `add_subplot` function, which works similar to the `np.subplot` functin. If you want just one axis of default size, you simply specify `111` (1 row, 1 column, figure 1)." ] }, { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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M21sNjg5aFLMuagBZKGTsuXHM2c06ZzTr/KTgc6lf5GHHxQOb70A7CMpADBzR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\n/OQocr0/HFoAAAAASUVORK5CYII=\n", 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\n", 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" ] }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax1 = fig.add_subplot(111)\n", + "ax1.plot(x, y, 'b');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want fancier options to create a grid of graphs, use the `subplot2grid` function, which allows you define a grid of graphs (3 by 3 in the example below). Then you can add graphs by specifying the row and column with the second argument (these are 0 based, so the upper left-hand corner is `loc=(0, 0)`). And you can, optionally, specify that a graph spans multiple graphs in the row or column direction. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 19, "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 6))\n", + "ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3)\n", + "ax2 = plt.subplot2grid((3, 3), (1, 0), rowspan=2)\n", + "ax3 = plt.subplot2grid((3, 3), (1, 1), rowspan=2, colspan=2)\n", + "ax1.plot(x, y)\n", + "ax2.plot(x, -y)\n", + "ax3.plot(x, y ** 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Matplotlib patches\n", + "The plotting package `matplotlib` includes a set of classes to define shapes, which are called *patches* in `matplotlib`. There are patches for many different shapes including circles, ellipses, polygons, wedges, and arrows. Here we learn how to draw these patches. \n", + "\n", + "The process for adding a patch to a graph is always the same. First you create an axis, then you create a patch object and you add the patch object to the axis. Each patch object has a few input arguments and a number of keyword arguments. The keyword arguments include: `ec` for edge color, `fc` for face color, `alpha` for transparancy, and `zorder` for the order in which they are plotted (the patch with the highest `zorder` value lies on top). The names of all patch classes start with a capital: `Circle`, `Ellipse`, `Polygon`, `Wedge`, `Arrow` (in fact, it is customary in Python to have all classes start with a capital). You need to import these classes from `matplotlib.patches` to be able to use them. Use the help system to learn about the required input arguments. The graph below contains two circles, where the smaller one is on top of the larger one. The face color of the graph is set to the same color as the small circle, so that it looks like the large circle has a hole. The aspect ratio of the axis is set to `'equal'` when the axis is created. The `autoscale` function needs to be called to set the limits of the axis such that the patches fit exactly in the axis. Alternatively, you can call the `ax.set_xlim` and `ax.set_ylim` functions to select limits of your own choice." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "from matplotlib.patches import Circle\n", "fig = plt.figure()\n", - "ax = fig.add_axes([.1, .1, .8, .8], facecolor='violet', aspect='equal')\n", + "ax = fig.add_subplot(111, facecolor='violet', aspect='equal')\n", "small = Circle(xy=(3, 5), radius=1, fc='violet', ec='violet', zorder=2)\n", "big = Circle(xy=(2, 4), radius=3, fc='dodgerblue', ec='dodgerblue', zorder=1)\n", "ax.add_patch(small)\n", @@ -698,9 +744,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -711,6 +755,39 @@ "Answers to Exercise 3" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 4. 3D - Spiral\n", + "\n", + "Consider the $x$, $y$, $z$ coordinates of a three-dimensional spiral\n", + "\n", + "$$x(t) = a\\cos(t)$$\n", + "$$y(t) = a\\sin(t)$$ \n", + "$$z(t) = bt$$\n", + "\n", + "where $a$ and $b$ are constants and $t$ is a parameter that varies. Write a function that takes $a$, $b$, and an array $t$ as input arguments and returns arrays $x$, $y$, and $z$. \n", + "\n", + "Next, import the 3D plotting capabilities of `matplotlib` with the command \n", + "`from mpl_toolkits.mplot3d import Axes3D`. \n", + "Plot a three-dimensional curve by specifying the keyword `projection='3d'` when creating the axis. You can than plot on that axis using `ax.plot` by simply specifying $x$, $y$, *and* $z$. Plot two spirals on the same graph. Use $a=4$ an $b=1$ for the first spiral, and $a=2$, $b=2$ for the second spiral; vary $t$ form 0 to 20 with 100 points. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Answers to Exercise 4" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -727,26 +804,26 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "total area: 67.8963703559\n" + "total area: 67.89637035591662\n" ] }, { "data": { - "image/png": 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C5ZrN6PXShEljvnOmMAWISf7S3W38yMtkqNVxKYwlyTSmADHJXwYHsz+oJR2G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\nEokwt2vdOtN0OdkxBUgeMWsWa6wKCpi4qYdPxO9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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -779,7 +856,7 @@ " areatot += c.area()\n", " c.plot(1.0)\n", "plt.axis('scaled')\n", - "print('total area:', areatot)" + "print(f'total area: {areatot}')" ] }, { @@ -793,26 +870,26 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "head at (20, 5) is: 0.296831810871\n" + "head at (20, 5) is: 0.2968318108714483\n" ] }, { "data": { - "image/png": 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fv3qIBRPGsHr2eFZljyNlkCtlBiprcfiZto5OdhSd4o0DFWw6WMGB8noA5qTFsip7PFed\nkxIwrYueNLS2c/7PX+cTWYn84TMLnI5jvOjGP25jX1kd79yxjFEhgfUh52zF1U08v7OUF/eUfziE\nd+b4GC6ensyy6cnMS48bkkXQnGItjgDS3tHJgfJ6thRU8e6RKrYdraahtZ2QIOHcifHctXoGK7PH\nkR4/fJrHUaNC+NyiCdz/5hEOnahn6thopyMZL9hRVMNbhyr59sppAV80wNUKuf3iLG6/OIujJxv5\ne145Gw9UcP+bR7h3Yz7R4SEsmpTA4skJnJeZwLRx0QFdSHpjLQ4fq248za7iU+wsPsX2ohp2FJ2i\nobUdcK1fsXhyAkumJHJBViIxfjSUdqjVNJ5myS/eYOn0ZO779Hyn4xgv+Pwft7G75BSbv3OxV2dg\ndlptUxtvHa7knfyTvHukiqLqJgCiw0NYMGEM56SPYV5GHHPTYomL8N9hvtbi8AOdnUrpqWYOltez\n/3gdeWW17C2ro6TGtYyqCEwbG83V56SSM3EM506MH1H3S8dEhvGFCzK5b1M+B8rrhnTJWuO8Dwpr\nePNQJXesmj6siwa4pnC/cm4KV851TRNfXN1EbmE17x+r4f2j1bx5qJIzn88z4iPITo1hVkosM8ZH\nM21cDCmx4QE3YstaHIN0ur2Touomjp1sJL+ygfwK13b4RD2Np//xhPSkxEhmpsSQnRrLvPQ4slNj\nh/0fVF9ONZ3mE7/YyJKsRO7/rPV1DCefe2Qr+8rq2PydZV6fut/f1be0saeklp0lp9hb6voQWVjV\n9OH70eEhZCVHMcW9TU6KIjMxkvT4CEKDfTe5h7U4hpCqUtV4mpKaZkpqmiipaaaouomiqiaKqpso\nqWmi6zxpSdGjmJIUxbUL0pg+LoZp46KZNi56xBeJ7sRFhPGFCybyuzfy2VdWx8wUa3UMB7nHqtl8\n+CTfXT19xBcNgOjwUBZPSWTxlH+sel3X0sah8nr2l9dzsLyOwycaeONABU/l/mOd9OAgIW3MaDLi\nI5iQEEH6mAjS4yNIGzOa1LjRxEeGOdZSsRaHW2en8tjWQo7XtlBe28Lx2haO1zZTVtvC6fbOjxw7\nJiKUjIRIMuIjmJgQQWZiJJmJkUxKjHJ8Hp5AU9vUxpJ73uD8SQk8eKNHH3aMn/vMw1s4WF7PW9+2\n1kZ/1TSepuBkI0dPNnL0ZAOF7g+ohVVN1Da3feTYUSFBpMaNZnxcOONiRjMudhSpcREDns7HWhwD\nEBQk3PP3gzS3dTA2JpyxMaPITo1lxaxxjI8NJ21MBGnxrkrvT/M/BbrYiFD+eckkfvPaIbYX1TDf\nnusIaG8dquSd/Cq+f8VMKxoDMCYyjAWRYSyY8PG/g9rmNkprmimuaaLsVLN7a6Gstpn3jpzkRH0r\nydGjfDIPnLU4uqhuPE3c6NCAelBpOGhsbWfprzYxIT6Cv916fsB1FBqXjk7lit+/TUNrG69986Jh\nMQQ3kHR0KnXNbQOeoLE/LQ6bVr2L+MgwKxoOiBwVwjeWTyW3sIZX9p1wOo4ZoOd2lLL/eB3/vmK6\nFQ0HBAeJz2b1dbRwiMhKETkoIvkickc374uI/M79/m4RsQH/w9Qnc9KYnBTJL146QFtHZ98/YPxK\nS1sHv37lIHPSYrli9nin4xgvc6xwiEgwcB+wCpgJ3CAiM886bBWQ5d5uAe73aUjjMyHBQdyxagYF\nJxt58n1bJTDQ/OmdY5TVtnDHqunWah8BnGxxLATyVbVAVU8DTwJrzzpmLfB/6rIFiBMR+zgzTC2f\nkczCifH89tVD1LW09f0Dxi9UNbTyh035XDw9mcWTE/v+ARPwnCwcqUDXj5Yl7n39PcYMEyLC96+Y\nSXXTaX7/+mGn4xgP/eqVgzSf7uC7q6c7HcX4yLDpHBeRW0QkV0RyKysrnY5jBmh2WizXLUjjT+8c\n40hlg9NxTB/ySmt58v1ibjx/IlOSbbLKkcLJwlEKpHf5Ps29r7/HAKCqD6pqjqrmJCUlDWlQ41v/\nvmI64aHB/Gz9PqejmF6oKj9+YS9jIsL42vIsp+MYH3KycLwPZIlIpoiEAdcD6846Zh1wo3t01SKg\nVlWP+zqo8a2k6FF89ZIpbDxYycYDFU7HMT1Yv/s47x+r4d8um0bsaHsodiRxrHCoajtwO/AysB94\nSlX3isitInKr+7ANQAGQDzwEfMWRsMbnblqcyaTESH66fh+t7R19/4DxqabT7fx8w35mpcTwqXPT\n+/4BM6w42sehqhtUdaqqTlbVu937HlDVB9yvVVVvc78/W1X9c5ENM+TCQoL4wZUzKTjZyCNvH3U6\njjnLvW/kU1bbwo/WzBq2ixWZng2bznEz/CydlsyKWWP5/ev5lJ5qdjqOccuvaOChzQVcOz+NcyfG\nOx3HOGBAhUNEHhzqIMZ05/tXzERRfvqCdZT7A1XlR+v2Eh4azB2rbPjtSNVj4RCR+B62BGC1DzOa\nESxtTAT/enEWf99bzqaD1lHutBf3HOft/JP8+4ppJEWPcjqOcUhvLY5KIBf4oMuW696SvR/NGJd/\n/oSro/yH6/bS0mYd5U5paG3nZ+tdHeKfOW+C03GMg3orHAXAUlXN7LJNUtVMwKYwNT4zKiSYn6zN\nprCqifs3HXE6zoj1m1cPcaK+hZ9elW0d4iNcb4Xjt0BPq+rc44UsxvRoSVYia+amcP+mIxTYE+U+\nt7eslj+9c5QbFmbYYlum58Khqvep6q4e3vu99yIZ073vXTGDUaFB/OD5vQzHBcj8VWenctezeYyJ\nCOM7K6xD3Hg4qkpEpnf9aowTkqPD+faKabydf5J1u8qcjjNiPPF+ETuLT/G9K2YQG2FPiBvPh+M+\nftZXYxzx6fMmMDctlp+u309ts0297m2V9a384qUDnD8pgavm2cTUxqW/z3FYj5hxVHCQcPfVs6lu\nbOWevx9wOs6wd/eL+2hp6+RnV2fbWvDmQ/bkuAk42amx3LQ4k8e3FbG9qMbpOMPW5sOVPLezjFuX\nTmZyUpTTcYwfscJhAtI3L5vKuJhwvvvMHluj3Ata2jr4/nN5ZCZG8pWlk52OY/xMfwuHDWUxfiFq\nVAg/vHIWB8rr+dM7NgniUPvDxnyOVTXx07XZhIcGOx3H+BlPC4ec9dUYx62YNZblM5L5zauHbRLE\nIXSksoH73zzCVfNSWJJla4ibj/O0cHzirK/GOE5E+NGaWQD8aN1eh9MMD6rK957NY3RoMHddPtPp\nOMZP9Vk4RORfgRAAVbVHdo1fSRsTwdeXZ/HqvhO8srfc6TgB77mdpbxXUMV3Vk23SQxNjzxpcYwF\nckXkKRFZKTYmz/iZm5dkMn1cND9at5fG1nan4wSs2qY2frZ+P+dkxHHDuRlOxzF+rM/CoarfA7KA\nR4CbgMMi8h8iYkMtjF8IDQ7i7quzKatt4XevH3Y6TsD6xcsHONXcxt1XzSbIJjE0vfCoj0NdEwOV\nu7d2XJMf/j8RsckOjV9YMCGeGxam8/DbRzlQXud0nICzvaiGx7cW8YXFE5mZEuN0HOPnPOnj+JqI\nfIBrRtx3gNmq+mVgAXCtl/MZ47HvrJxOTHgI338uzyZB7IeOTuX7z+UxNmYUX790qtNxTADwpMUR\nD1yjqitU9W+q2gagqp3AFV5NZ0w/xEWEcceq6bx/rIant5c6HSdgPLqlkL1ldXz/iplEjQpxOo4J\nAJ70cfxQVQt7eG//QC7qXoL2VRE57P7a7QT/InJMRPaIyE4RyR3ItczIct2CdOZnxPHzDfupbbJJ\nEPtSWd/Kr145yJIpiVw+e7zTcUyAcGrKkTuA11U1C3jd/X1PlqnqPFXN8U00E8iCgoSfXpVNTdNp\nfvXKQafj+L2fb9hPS1sHP147yyYxNB5zqnCsBf7sfv1n4CqHcphhaFZKLDeeP5FHtxaSV1rrdBy/\nte1oNc/sKOWWCyfZJIamX5wqHGNV9bj7dTmuZ0W6o8BrIvKBiNzim2hmOPjGpVOJjwjjR+tstcDu\ndHQqP1y3l5TYcG5bNsXpOCbAeDKqql5E6s7aikXkWRGZ1MvPvSYied1sa7se5x7q29Nf9hJVnQes\nAm4TkQt7ud4tIpIrIrmVlZV9/c8yw1zs6FC+vXIauYU1tlpgN57YVsT+43V89/IZRIRZh7jpH09+\nY34LlOBa/U+A64HJwHbgj8DS7n5IVZf3dEIROSEi41X1uIiMByp6OEep+2uFiDwLLATe6uHYB4EH\nAXJycuwjpuG6Bek8trWI/9iwn+UzxhJpI4YAOOXu/1k0Kd46xM2AeHKrao2q/o+q1qtqnfsf6BWq\n+ldcDwIOxDrg8+7XnweeP/sAEYkUkegzr4HLgLwBXs+MQEFBwg+vnMWJulbu25jvdBy/8V+vHqKu\nuY0frbEOcTMwnhSOJhH5pIgEubdPAi3u9wb6yf4/gUtF5DCw3P09IpIiIhvcx4wF3haRXcA24EVV\n/fsAr2dGqAUTxnDN/FQe3nyUwqpGp+M47kB5HY9uKeRziyYwfZw9IW4GxpPC8Rngc7huJ51wv/6s\niIwGbh/IRVW1SlUvUdUsVV2uqtXu/WWqutr9ukBV57q3Wap690CuZcwdK6cTEiz850sje41yVeXu\nF/cTHR7KN+wJcTMInjwAWKCqV6pqoqomuV/nq2qzqr7ti5DGDEZyTDj/cuFkXsor5/1j1U7Hccym\nQ5VsPnySr16SRVxEmNNxTADzZFRVkoh8V0QeFJE/ntl8Ec6YofKlCzMZFxPOz9bvo7Nz5I2daO/o\n5D9e3M/EhAg+t2iC03FMgPPkVtXzQCzwGvBil82YgBERFsK/rZjGrpJaXtg98obn/jW3mMMVDdyx\nagZhIU49vmWGC0/GJ0ao6ne8nsQYL7vmnFT+9M5R7vn7QVbMGkd4aLDTkXyivqWN/3rlEAsnxrNi\nVk/P2hrjOU8+eqwXkdVeT2KMlwUFCXddPoPSU838+d1jTsfxmYfeKqCq8TR3XT7Dht+aIeFJ4fga\nruLR4n6KvF5EbKUcE5AWT07kwqlJ3P/mEepahv/suScbWnn47aNcPns8c9PjnI5jhglPRlVFq2qQ\nqoa7X0erqg0ANwHr2yumcaqpjYfeKnA6itfdtzGf1vZOvnmZDb81Q8ejXjIRWSMiv3JvtniTCWjZ\nqbFcPmc8j7x9lMr6VqfjeE1JTROPbSniugVpNvutGVKeDMf9T1y3q/a5t6+JyM+9HcwYb/rWpVNp\nbe/kD5uG71Qk//3aYRD42vIsp6OYYcaTFsdq4FJV/aOq/hFYCVzu3VjGeNekpCiuW5DGY1uKKKlp\ncjrOkMuvaODp7SXcuGgC42NHOx3HDDOeDuju2qsW640gxvjaVy9xfRK/f9MRh5MMvd+/cZjw0GC+\nvHSy01HMMORJ4fg5sENE/ldE/gx8ANi8USbgpcSN5rqcNP6WW8Lx2man4wyZgsoGXthVxufOn0BC\n1Cin45hhyJNRVU8Ai4BngKeB891TqhsT8L68dDKdqvzPm8NnhNV9G48QFhLElz7R4zprxgxKj4VD\nROaf2YDxuBZzKgFS3PuMCXhpYyK4Zn4qj28roqKupe8f8HNFVU08t7OUTy+cQKK1NoyX9DblyK97\neU+Bi4c4izGOuG3ZFJ7eXsqDbxXwvStmOh1nUP6wKZ/gIOFfLrLWhvGeHguHqi7zZRBjnDIhIZK1\nc1N4bGsRty6dHLCf1Etqmnh6ewk3LMxgbEy403HMMObpA4DZ7lUAbzyzeTuYMb70lWVTaGnvCOg5\nrB55+yiq8C8X2Ugq412ePAD4Q+D37m0ZcA+wxsu5jPGpKclRLJ8xlke3FNJ8usPpOP1W29zGU+8X\ns2ZuCqlx9tyG8S5PWhz/BFwClKvqF4C52LMcZhj65yWZ1DS18cyOEqej9NuT24poPN3BzUsynY5i\nRgBPCkezqnYC7SISg2vt8XTvxjLG9xZmxjMnLZZHNh8NqFUC2zo6+d93j3H+pASyU+0znfE+TwpH\nrojEAQ/hevhvO/CeV1MZ4wAR4YtLMik42cjGgxVOx/HYhj3HOV7bwpcutNaG8Q1PHgD8iqqeUtUH\ngEuBz7tvWQ2YiFwnIntFpFNEcno5bqWIHBSRfBG5YzDXNMYTq2ePJyU2nIc3H3U6ikdUlYc2FzAp\nKZKlU5PBojxvAAATx0lEQVSdjmNGiH4tPqyqx1R19xBcNw+4BnirpwNEJBi4D1gFzARuEJHAHmRv\n/F5ocBA3XTCR9wqqyCutdTpOn7YerSavtI4vLskkKMhW9zO+4ciq9aq6X1UP9nHYQiBfVQtU9TTw\nJLDW++nMSHf9wgwiwoL5y3uFTkfp01/eKyQuIpRr56c5HcWMII4UDg+lAsVdvi9x7zPGq2LCQ1kz\nN4V1u8qo9+PlZSvrW3l5bznXzk8jPDTY6ThmBOltrqr43ra+Tiwir4lIXjebV1oNInKLiOSKSG5l\nZaU3LmFGkBsWZtDc1sFzO8ucjtKjp7eX0N6p3LDQBjka3+ptrqoPcM1JJUAGUON+HQcUAb0O4VDV\n5YPMVspHh/2muff1dL0HgQcBcnJyAmcspfFLc9JimZUSw+Nbi/jseRmI+Ff/QWen8sS2IhZmxjMl\nOdrpOGaE6bHFoaqZqjoJeA24UlUTVTUBuAJ4xQfZ3geyRCRTRMKA64F1PriuMYgINyzMYP/xOnaX\n+F8n+XsFVRRWNfHphRlORzEjkCd9HItUdcOZb1T1JWDxYC4qIleLSAlwPvCiiLzs3p8iIhvc12kH\nbgdeBvYDT6nq3sFc15j+WDsvhYiwYB7fWuR0lI95fGsRcRGhrMwe53QUMwJ5UjjKROR7IjLRvd0F\nDOrGr6o+q6ppqjpKVceq6gr3/jJVXd3luA2qOlVVJ6uqrTpofCraTzvJrVPcOM2TwnEDkAQ8696S\n3fuMGfbOdJKv2+U/neTPfNgpbrepjDN66xwHQFWrga/5IIsxfmdOWixTx0bx/I4yPnPeBKfjAPDc\nzjLOyYhjSnKU01HMCOXJtOpJIvJLEdkgIm+c2XwRzhiniQhr5qaw7Vg1paeanY7DoRP17D9ex9q5\nKU5HMSOYJ7eqHgMO4Bp++2PgGK4RT8aMCGvmup47fcEPblet21lGkMDlc6xwGOd4UjgSVPURoE1V\n31TVm7H1xs0IkpEQwTkZcaxz+GFAVWXdrjIumJJIUnRgLm9rhgdPCseZ4STHReRyETkH6PPJcWOG\nkzVzU9h3vI7DJ+ody7Cz+BRF1U2ssdtUxmGeFI6fiUgs8C3g34CHgW94NZUxfubyOeMJEhwdXfX8\nzjLCQoJYYc9uGId5sh7HelWtVdU8VV2mqgtU1Z7gNiNKcnQ4F0xJZN2uMlR9P6NNR6eyfvdxLpme\nTEx4qM+vb0xXnoyqmioir4tInvv7OSLyPe9HM8a/XDk3hcKqJnY5MAXJe0eqONnQarepjF/w5FbV\nQ8CduPs63As5Xe/NUMb4o5XZ4wgLDuLF3b6/XfXinjKiRoWwbLqt8mec50nhiFDVbWfta/dGGGP8\nWUx4KIsmJ/D6Ad+uR66qvL6/goumJtkUI8YveFI4TorIZFxTrCMi/wQc92oqY/zUJdOTKahs5OjJ\nRp9dM6+0jor6Vi621obxE54UjtuA/wGmi0gp8HXgy15NZYyfOvOP9+v7T/jsmq8fOIEILJ2W5LNr\nGtMbT0ZVFbgXZUoCpqvqElU95vVkxvih9PgIpo2N5g0f3q5640AF8zPGkBBlD/0Z/9DnJIciMgq4\nFpgIhJxZCU1Vf+LVZMb4qYtnJPPQWwXUtbR5fWjsiboWdpfU8u8rpnn1Osb0hye3qp4H1uLqEG/s\nshkzIi2fkUx7p/LWIe+vbb/R3bK5ZIb1bxj/0WeLA0hT1ZVeT2JMgJiXPoYxEaG8sb+CK7w82eDr\nBypIjRvNtLG2rrjxH560ON4VkdleT2JMgAgOEpZNS2bjwQo6Or33FHlLWwdvHz7JJTOSOXOL2Bh/\n0GPhEJE9IrIbWAJsF5GDIrK7y35jRqxLZoylpqmNHUU1XrvGewVVNLd12DBc43d6u1V1hc9SGBNg\nPjE1kZAg4Y0DFeRM9M5k0RsPVDA6NJhFkxK8cn5jBqrHwqGqhb4MYkwgiQkPZW56HFsKqrx2jS0F\nVZybGW9Pixu/40kfx5ATketEZK+IdIpITi/HHXPfGtspIrm+zGhMX87LjGd3SS1Np4d+Bp7qxtMc\nOtHAeZm29I3xP44UDiAPuAZ4y4Njl6nqPFXtscAY44TzJiXQ3qlsLzw15OfedtTVklk0yQqH8T+O\nFA5V3a+qB524tjFDZcGEMQQHCVuPDv3tqi0F1YSHBjE7NW7Iz23MYDnV4vCUAq+JyAcicovTYYzp\nKmpUCNmpsWwtqB7yc289Ws2CCWMIC/H3P1EzEnntt1JEXhORvG62tf04zRJVnQesAm4TkQt7ud4t\nIpIrIrmVld5/otcYcPVz7Cw+RUtbx5Cds7apjQPldSycaKOpjH/yWuFQ1eWqmt3N9nw/zlHq/loB\nPAss7OXYB1U1R1VzkpJsFlHjG+dlxnO6o5MdRUPXz7HtWDWqcJ71bxg/5bftYBGJFJHoM6+By3B1\nqhvjN3ImxiPCkPZzbC2oIiwkiHnp1r9h/JNTw3GvFpES4HzgRRF52b0/RUQ2uA8bC7wtIruAbcCL\nqvp3J/Ia05PY0aHMHB8zpP0cW49WMy89zp7fMH7Lk0kOh5yqPovr1tPZ+8uA1e7XBcBcH0czpt/O\ny0zgsa2FtLZ3MCpkcP/Y17W0sbesltuXTRmidMYMPb+9VWVMoDhvUjyt7Z3sLqkd9Lk+KKyhU13P\niBjjr6xwGDNIC91zVW0dgulHthZUExoszM8YM+hzGeMtVjiMGaQxkWFMSY5iZ/HgR1btLK5hZkos\no8Osf8P4LyscxgyB2amx7Ckd3K2qzk5lb2kds1NjhiiVMd5hhcOYITArJYYTda1U1LcM+BxF1U3U\nt7aTnRI7hMmMGXpWOIwZArNTXf/Y7y2rG/A5zrRYslOtcBj/ZoXDmCEwM8V1eylvECOr8spqCQsO\nYqqtL278nBUOY4ZAdHgomYmR5JUNvHDsLa1j2rhom9jQ+D37DTVmiGSnxpJXOrBbVarKntJasq1j\n3AQAKxzGDJHslBhKTzVT03i63z9bUtNMbXMbs6xj3AQAKxzGDJEzndoDuV2V5+4Yn20d4yYAWOEw\nZoicGUY7kNtVeWW1hAQJ08ZZx7jxf1Y4jBkisRGhpMeP/rD10B95pXVkjY22GXFNQLDCYcwQyk6J\n7fetKlUlr7SW7BTrGDeBwQqHMUMoOzWWwqomapvbPP6Z8roWqhpP24N/JmBY4TBmCGV/+AS5562O\nPSX2xLgJLFY4jBlCZ2437e1HB3leWR1BAjPH260qExiscBgzhBKiRjE+NrxfLY59ZbVMToqyqdRN\nwLDCYcwQmzo2msMVDR4ff7iigak2DNcEECscxgyxrOQo8isa6OjUPo9taeugqLqJrOQoHyQzZmhY\n4TBmiGWNjaK1vZPSmuY+jz1S2YAqZCVbi8MEDkcKh4j8UkQOiMhuEXlWROJ6OG6liBwUkXwRucPX\nOY0ZiCnuInC4or7PY/Pdt7SyxlqLwwQOp1ocrwLZqjoHOATcefYBIhIM3AesAmYCN4jITJ+mNGYA\nprhvO3nSz3H4RAPBQcLEhEhvxzJmyDhSOFT1FVVtd3+7BUjr5rCFQL6qFqjqaeBJYK2vMhozULGj\nQxkbM4rDJzwoHBX1TEyIsDU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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -838,7 +915,7 @@ "h = 0.0\n", "for w in wells:\n", " h += w.head(20, 5)\n", - "print('head at (20, 5) is:', h)\n", + "print(f'head at (20, 5) is: {h}')\n", "\n", "x = np.linspace(-40, 40, 101)\n", "h = np.zeros_like(x)\n", @@ -861,19 +938,19 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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SuiIRkZqJLKiZWZGZfWRmr0TVpogkzz77+HNgzpvnj5o84ohkrOLfpIkPkKNHw8SJcNll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\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -910,7 +987,53 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Back to Exercise 3" + "Back to Exercise 3\n", + "\n", + "Answers to Exercise 4" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "def spiral(a, b, t):\n", + " x = a * np.cos(t)\n", + " y = a * np.sin(t)\n", + " z = b * t\n", + " return x, y, z\n", + "\n", + "t = np.linspace(0, 20, 100)\n", + "x1, y1, z1 = spiral(4, 1, t)\n", + "x2, y2, z2 = spiral(2, 2, t)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.plot(x1, y1, z1)\n", + "ax.plot(x2, y2, z2);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Back to Exercise 4" ] } ], @@ -930,9 +1053,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.7.0" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebook13_regression1/py_exploratory_comp_13_sol.ipynb b/notebook13_regression1/py_exploratory_comp_13_sol.ipynb index a73808d..3cbe5a0 100644 --- a/notebook13_regression1/py_exploratory_comp_13_sol.ipynb +++ b/notebook13_regression1/py_exploratory_comp_13_sol.ipynb @@ -22,9 +22,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -46,11 +44,11 @@ "### Root mean square error\n", "One way to quantify the fit between data and a model is to compute the root mean square error. The error is defined as the difference between the observed value and the modeled value. Another term for the error is the residual. If the error of data point $i$ is written as $\\varepsilon_i$, and the total number of observations is $N$, then the sum of squared errors $S$ is\n", "\n", - "$$S = \\sum{\\varepsilon_i^2}$$\n", + "$$E = \\sum{\\varepsilon_i^2}$$\n", "\n", "When the total number of observations is $N$, the root mean square error $E$ is computed as\n", "\n", - "$$E=\\sqrt{\\frac{1}{N}S}=\\sqrt{\\frac{1}{N}\\sum{\\varepsilon_i^2}}$$\n", + "$$E_s=\\sqrt{\\frac{1}{N}S}=\\sqrt{\\frac{1}{N}\\sum{\\varepsilon_i^2}}$$\n", "\n", "The root mean square error is an estimate of the goodness of fit and can be computed for any model and any dataset." ] @@ -66,9 +64,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -90,9 +86,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -114,9 +108,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def func(x, a, b):\n", @@ -133,15 +125,13 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "optimal parameters: [ 6.07744372 42.58245717]\n" + "optimal parameters: [ 6.07744372 42.58245717]\n" ] } ], @@ -170,9 +160,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -196,18 +184,18 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -230,9 +218,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def sse(a, b, x=xdata, y=ydata):\n", @@ -250,9 +236,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -272,24 +256,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "What we can do is compute the `sse` function for a larger number of $a$ and $b$ values. If we do that on a regular grid, we can create contours of the `sse` function. The `sse` function is constant along any contour. A contour map of the `sse` function is similar to an elevation map. The goal is now to find the combination of $a$ and $b$ that gives the smallest value of the sum of squared errors. In the graph below, you can see that the smallest value of `sse` is obtained at $a\\approx 0.4$, $b\\approx 1.3$ (you have to look closely for the darkest blue in the figure; the area beyond the yellow is $S>10$)." + "What we can do is compute the `sse` function for a larger number of $a$ and $b$ values. If we do that on a regular grid, we can create contours of the `sse` function. The `sse` function is constant along any contour. A contour map of the `sse` function is similar to an elevation map. The goal is now to find the combination of $a$ and $b$ that gives the smallest value of the sum of squared errors. In the graph below, you can see that the smallest value of `sse` is obtained at $a\\approx 0.4$, $b\\approx 1.3$ (you have to look closely for the darkest blue in the figure; the area beyond the yellow is $E>10$)." ] }, { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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ef+Vi0JTXv1iEM/Q+Y/vk9reNLeBrzt62z+b1Wx8fd5t5XQ/KSkL1bkxySCgu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\ntwAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -364,9 +348,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -434,9 +416,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -457,16 +437,14 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "p value: 0.178912375022 s value: 0.115470053838\n" + "p value: 0.1789123750220667 s value: 0.11547005383792511\n" ] } ], @@ -478,16 +456,14 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "upper and lower bound for significance level 0.178912375022 is: -0.400000000004 0.400000000004\n" + "upper and lower bound for significance level 0.1789123750220667 is: -0.4000000000044717 0.4000000000044717\n" ] } ], @@ -506,11 +482,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p-value from t-distribution: [[0.33333333 0.322417 0.31213621 ... 0.12433906 0.12266646 0.12103772]\n", + " [0.33333333 0.322417 0.31213621 ... 0.12433906 0.12266646 0.12103772]\n", + " [0.33333333 0.322417 0.31213621 ... 0.12433906 0.12266646 0.12103772]\n", + " ...\n", + " [0.33333333 0.322417 0.31213621 ... 0.12433906 0.12266646 0.12103772]\n", + " [0.33333333 0.322417 0.31213621 ... 0.12433906 0.12266646 0.12103772]\n", + " [0.33333333 0.322417 0.31213621 ... 0.12433906 0.12266646 0.12103772]]\n" + ] + } + ], "source": [ "print('p-value from t-distribution:', 2 * (1 - t.cdf(a, 1, loc=0, scale=s)))" ] @@ -542,9 +530,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -571,11 +557,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slope, intercept: 6.077443700312609 42.58245735877516\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from scipy.stats import linregress\n", "x, y = np.loadtxt('xydatafit.dat')\n", @@ -601,11 +605,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "x, y = np.loadtxt('xydatafit.dat')\n", "a, b, c = np.polyfit(x, y, 2)\n", @@ -629,11 +644,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from scipy.optimize import curve_fit\n", "\n", @@ -662,10 +688,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "def sse(a, b, x=xdata, y=ydata):\n", @@ -675,11 +699,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "optimal values of a and b: 0.4 1.333333333333333\n", + "sse: 0.6666666666666667\n" + ] + } + ], "source": [ "xdata = np.array([5.0, 10.0, 15.0])\n", "ydata = np.array([3.0, 6.0, 7.0])\n", @@ -693,11 +724,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slope and intercept according to linregress: 0.4 1.333333333333333\n" + ] + } + ], "source": [ "from scipy.stats import linregress\n", "slope, intercept, r, p, s = linregress(xdata, ydata)\n", @@ -706,11 +743,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.plot(xdata, ydata, 'bo', label='observed')\n", "plt.xlim(0, 20)\n", @@ -732,11 +780,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r squared according to formula: 0.923076923076923\n", + "r squared according to linregress: 0.9230769230769231\n" + ] + } + ], "source": [ "yfit = a * xdata + b\n", "print('r squared according to formula:', end=' ')\n", @@ -746,11 +801,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "std of slope according to formula: 0.11547005383792515\n", + "std of slope according to linregress: 0.11547005383792511\n" + ] + } + ], "source": [ "print('std of slope according to formula:', end=' ')\n", "print(np.sqrt(np.sum((ydata - yfit)**2) / (N - 2)) / np.sqrt(np.sum((xdata - np.mean(xdata)) ** 2)))\n", @@ -759,11 +821,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from scipy.stats import t\n", "x = np.linspace(-1.5, 1.5, 100)\n", @@ -788,11 +861,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of 1000 experiments where p < 0.05: 52\n" + ] + } + ], "source": [ "count = 0\n", "for i in range(1000):\n", @@ -828,9 +907,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.7.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebook1_basics_plotting/py_exploratory_comp_1.ipynb b/notebook1_basics_plotting/py_exploratory_comp_1.ipynb index f0e5fbe..0c5d78d 100644 --- a/notebook1_basics_plotting/py_exploratory_comp_1.ipynb +++ b/notebook1_basics_plotting/py_exploratory_comp_1.ipynb @@ -35,7 +35,8 @@ "metadata": {}, "source": [ "Note that the extra spaces are added to make the code more readable. \n", - "`2 * 3` works just as well as `2*3`. But is it highly recommended to use the additional spaces (in fact, it is considered good style to do so).\n", + "`2 * 3` works just as well as `2*3`. And it is considered good style. Use the extra spaces in all your Notebooks.\n", + "\n", "When you are programming, you want to store your values in variables" ] }, @@ -54,7 +55,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Both `a` and `b` are now variables. Each variable has a type. In this case, they are both integers (whole numbers). To write the value of a variable to the screen, use the `print` function (the last statement of a code cell is automatically printed to the screen if it is not stored in a variable, as was shown above)" + "Both `a` and `b` are now variables. Each variable has a type. In this case, they are both integers (whole numbers). To write the value of a variable to the screen, use the `print` function (the last statement of a code cell is automatically printed to the screen if it is not stored in a variable, as was shown above). Note that multiplication of two integers results in an integer, but division of two integers results in a float (a number with decimal places). " ] }, { @@ -73,7 +74,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can add some text to the `print` function by putting the text between quotes (either single or double quotes work as long as you use the same at the beginning and end), and separate the text string and the variable by a comma" + "You can add some text to the `print` function by putting the text string between quotes (either single or double quotes work as long as you use the same at the beginning and end), and separate the text string and the variable by a comma" ] }, { @@ -106,8 +107,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Exercise 1, First Python code\n", - "Compute the value of the polynomial $y=ax^2+bx+c$ at $x=-2$, $x=0$, and $x=2$ using $a=1$, $b=1$, $c=-6$." + "### Exercise 1a, First Python code\n", + "Compute the value of the polynomial $y=ax^2+bx+c$ at $x=-2$, $x=0$, and $x=2.1$ using $a=1$, $b=1$, $c=-6$ and print the results to the screen." ] }, { @@ -121,7 +122,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Answer to Exercise 1" + "Answer to Exercise 1a" ] }, { @@ -145,7 +146,59 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "(Note for Python 2 users: `1/3` gives zero in Python 2, as the division of two integers returns an integer. Use `1.0/3` instead." + "(Note for Python 2 users (you should really change to Python 3!): `1/3` gives zero in Python 2, as the division of two integers returned an integer in Python 2). The above print statement looks pretty ugly with 16 values of 3 in a row. A better and more readable way to print both text and the value of a variable to the screen is to use what are called f-strings. f-strings allow you to insert the value of a variable anywhere in the text by surrounding it with braces `{}`. The entire text string needs to be between quotes and be preceded by the letter `f`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 1\n", + "b = 3\n", + "c = a / b\n", + "print(f'{a} divided by {b} gives {c}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The complete syntax between braces is `{variable:width.precision}`. When `width` and `precision` are not specified, Python will use all digits and figure out the width for you. If you want a floating point number with 3 decimals, you specify the number of digits, `3`, followed by the letter `f` for floating point (you can still let Python figure out the width by not specifying it). If you prefer exponent (scientific) notation, replace the `f` by an `e`. The text after the `#` is a comment in the code. Any text on the line after the `#` is ignored by Python. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f'{a} divided by {b} gives {c:.3f}') # three decimal places\n", + "print(f'{a} divided by {b} gives {c:10.3f}') # width 10 and three decimal places\n", + "print(f'{a} divided by {b} gives {c:.3e}') # three decimal places scientific notation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 1b, First Python code using f-strings\n", + "Compute the value of the polynomial $y=ax^2+bx+c$ at $x=-2$, $x=0$, and $x=2.1$ using $a=1$, $b=1$, $c=-6$ and print the results to the screen using f-strings and 2 decimal places." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Answer to Exercise 1b" ] }, { @@ -153,7 +206,7 @@ "metadata": {}, "source": [ "### More on variables\n", - "Once you have created a variable in a Python session, it will remain in memory, so you can use it in other cells as well. For example, the variable `a`, which was defined earlier in this Notebook, still exist. It will be `6` unless you changed it in Exercise 1. " + "Once you have created a variable in a Python session, it will remain in memory, so you can use it in other cells as well. For example, the variables `a` and `b`, which were defined two code cells above in this Notebook, still exist. " ] }, { @@ -162,7 +215,8 @@ "metadata": {}, "outputs": [], "source": [ - "print('a:', a)" + "print(f'the value of a is: {a}')\n", + "print(f'the value of b is: {b}')" ] }, { @@ -179,7 +233,7 @@ "metadata": {}, "source": [ "### Basic plotting and a first array\n", - "Plotting is not part of standard Python, but a nice package exist to create pretty graphics (and ugly ones, if you want). A package is a library of functions for a specific set of tasks. There are many Python packages and we will use several of them. The graphics package we use is called `matplotlib`. To be able to use the plotting functions in `matplotlib` we have to import it. We will learn several different ways of importing packages. For now, we import the plotting part of `matplotlib` and call it `plt`. Before we import `matplotlib`, we tell the Jupyter Notebook to show any graphs inside this Notebook and not in a separate window (more on these commands later). " + "Plotting is not part of standard Python, but a nice package exists to create pretty graphics (and ugly ones, if you want). A package is a library of functions for a specific set of tasks. There are many Python packages and we will use several of them. The graphics package we use is called `matplotlib`. To be able to use the plotting functions in `matplotlib`, we have to import it. We will learn several different ways of importing packages. For now, we import the plotting part of `matplotlib` and call it `plt`. Before we import `matplotlib`, we tell the Jupyter Notebook to show any graphs inside this Notebook and not in a separate window using the `%matplotlib inline` command (more on these commands later). " ] }, { @@ -212,7 +266,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's try to plot $y$ vs $x$ for $x$ going from $-4$ to $+4$ for the polynomial in the exercise above. To do that, we need to evaluate $y$ at a bunch of points. A sequence of values of the same type is called an array (for example an array of integers or floats). Array functionality is available in the package `numpy`. Let's import `numpy` and call it `np`, so that any function in the `numpy` package may be called as `np.function`. " + "Let's try to plot $y$ vs $x$ for $x$ going from $-4$ to $+4$ for the polynomial\n", + "$y=ax^2+bx+c$ with $a=1$, $b=1$, $c=-6$.\n", + "To do that, we need to evaluate $y$ at a bunch of points. A sequence of values of the same type is called an array (for example an array of integers or floats). Array functionality is available in the package `numpy`. Let's import `numpy` and call it `np`, so that any function in the `numpy` package may be called as `np.function`. " ] }, { @@ -272,16 +328,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that *one hundred* `y` values are computed in the simple line `y = a * x ** 2 + b * x + c`. The text after the `#` is a comment in the code. Any text on the line after the `#` is ignored by Python. Python treats arrays in the same fashion as it treats regular variables when you perform mathematical operations. The math is simply applied to every value in the array (and it runs much faster than when you would do every calculation separately). \n", + "Note that *one hundred* `y` values are computed in the simple line `y = a * x ** 2 + b * x + c`. Python treats arrays in the same fashion as it treats regular variables when you perform mathematical operations. The math is simply applied to every value in the array (and it runs much faster than when you would do every calculation separately). \n", "\n", - "You may wonder what the statement `[]` is (the numbers on your machine may look different). This is actually a handle to the line that is created with the last command in the code block (in this case `plt.plot(x, y)`). Remember: the result of the last line in a code cell is printed to the screen, unless it is stored in a variable. You can tell the Notebook not to print this to the screen by putting a semicolon after the last command in the code block (so type `plot(x, y);`). We will learn later on that it may also be useful to store this handle in a variable." + "You may wonder what the statement like `[]` is (the numbers above on your machine may look different). This is actually a handle to the line that is created with the last command in the code block (in this case `plt.plot(x, y)`). Remember: the result of the last line in a code cell is printed to the screen, unless it is stored in a variable. You can tell the Notebook not to print this to the screen by putting a semicolon after the last command in the code block (so type `plot(x, y);`). We will learn later on that it may also be useful to store this handle in a variable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `plot` function can take many arguments. Looking at the help box of the `plot` function (by typing `plt.plot(` and then shift-tab) gives you a lot of help. Typing `plt.plot?` gives a new scrollable subwindow at the bottom of the notebook, showing the documentation on `plot`. Click the x in the upper right hand corner to close the subwindow again." + "The `plot` function can take many arguments. Looking at the help box of the `plot` function, by typing `plt.plot(` and then shift-tab, gives you a lot of help. Typing `plt.plot?` gives a new scrollable subwindow at the bottom of the notebook, showing the documentation on `plot`. Click the x in the upper right hand corner to close the subwindow again." ] }, { @@ -321,6 +377,29 @@ "Names may be added along the axes with the `xlabel` and `ylabel` functions, e.g., `plt.xlabel('this is the x-axis')`. Note that both functions take a string as argument. A title can be added to the figure with the `plt.title` command. Multiple curves can be added to the same figure by giving multiple plotting commands in the same code cell. They are automatically added to the same figure." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### New figure and figure size\n", + "\n", + "Whenever you give a plotting statement in a code cell, a figure with a default size is automatically created, and all subsequent plotting statements in the code cell are added to the same figure. If you want a different size of the figure, you can create a figure first with the desired figure size using the `plt.figure(figsize=(width, height))` syntax. Any subsequent plotting statement in the code cell is then added to the figure. You can even create a second figure (or third or fourth...)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(10, 3))\n", + "plt.plot([1, 2, 3], [2, 4, 3], linewidth=6)\n", + "plt.title('very wide figure')\n", + "plt.figure() # new figure of default size\n", + "plt.plot([1, 2, 3], [1, 3, 1], 'r')\n", + "plt.title('second figure');" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -343,6 +422,74 @@ "Answer to Exercise 2" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Style\n", + "\n", + "As was already mentioned above, good coding style is important. It makes the code easier to read so that it is much easier to find errors and bugs. For example, consider the code below, which recreates the graph we produced earlier (with a wider line), but now there are no additional spaces inserted" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a=1\n", + "b=1\n", + "c=-6\n", + "x=np.linspace(-4,4,100)\n", + "y=a*x**2+b*x+c#Compute y for all x values\n", + "plt.plot(x,y,linewidth=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The code in the previous code cell is difficult to read. Good style includes at least the following:\n", + "* spaces around every mathematical symbol (`=`, `+`, `-`, `*`, `/`), but not needed around `**`\n", + "* spaces between arguments of a function\n", + "* no spaces around an equal sign for a keyword argument (so `linewidth=3` is correct)\n", + "* one space after every comma\n", + "* one space after each `#`\n", + "* two spaces before a `#` when it follows a Python statement\n", + "* no space between the function name and the list of arguments. So `plt.plot(x, y)` is good style, and `plt.plot (x, y)` is not good style.\n", + "\n", + "These rules are (a very small part of) the official Python style guide called PEP8. When these rules are applied, the code is *much* easier to read, as you can see below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 1\n", + "b = 1\n", + "c = -6\n", + "x = np.linspace(-4, 4, 100)\n", + "y = a * x**2 + b * x + c # Compute y for all x values\n", + "plt.plot(x, y, linewidth=3);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use correct style in all other exercises and all Notebooks to come. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 2b. First graph revisited\n", + "Go back to your Exercise 2 and apply correct style. " + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -379,7 +526,7 @@ "metadata": {}, "source": [ "### Exercise 4, Subplots and fancy tick markers\n", - "Load the average monthly air temperature and seawater temperature for Holland. Create one plot with two graphs above each other using the subplot command (use `plt.subplot?`). On the top graph, plot the air and sea temperature. Label the ticks on the horizontal axis as 'jan', 'feb', 'mar', etc., rather than 0,1,2,etc. Use `plt.xticks?` to find out how. In the bottom graph, plot the difference between the air and seawater temperature. Add legends, axes labels, the whole shebang." + "Load the average monthly air temperature and seawater temperature for Holland. Create one plot with two graphs above each other using the `subplot` command (use `plt.subplot?` to find out how). On the top graph, plot the air and sea temperature. Label the ticks on the horizontal axis as 'jan', 'feb', 'mar', etc., rather than numbers. Use `plt.xticks?` to find out how. In the bottom graph, plot the difference between the air and seawater temperature. Add legends, axes labels, the whole shebang." ] }, { @@ -401,7 +548,48 @@ "metadata": {}, "source": [ "### Colors\n", - "There are five different ways to specify colors in matplotlib plotting; you may read about it [here](http://matplotlib.org/examples/pylab_examples/color_demo.html). A useful way is to use the html color names. The html codes may be found, for example, [here](http://en.wikipedia.org/wiki/Web_colors). But the coolest way is probably to use the xkcd names, which need to be prefaced by the `xkcd:`. The xkcd list of color names is given by [xkcd](https://xkcd.com/color/rgb/) and includes favorites such as 'baby puke green' and a number of brown colors vary from `poo` to `poop brown` and `baby poop brown`. Try it out:" + "If you don't specify a color for a plotting statement, `matplotlib` will use its default colors. The first three default colors are special shades of blue, orange and green. The names of the default colors are a capital `C` followed by the number, starting with number `0`. For example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot([0, 1], [0, 1], 'C0')\n", + "plt.plot([0, 1], [1, 2], 'C1')\n", + "plt.plot([0, 1], [2, 3], 'C2')\n", + "plt.legend(['default blue', 'default orange', 'default green']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are five different ways to specify your own colors in matplotlib plotting; you may read about them [here](http://matplotlib.org/examples/pylab_examples/color_demo.html). A useful way is to use the html color names. The html codes may be found, for example, [here](http://en.wikipedia.org/wiki/Web_colors). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "color1 = 'fuchsia'\n", + "color2 = 'lime'\n", + "color3 = 'DodgerBlue'\n", + "plt.plot([0, 1], [0, 1], color1)\n", + "plt.plot([0, 1], [1, 2], color2)\n", + "plt.plot([0, 1], [2, 3], color3)\n", + "plt.legend([color1, color2, color3]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The coolest (and nerdiest) way is probably to use the xkcd names, which need to be prefaced by the `xkcd:`. The xkcd list of color names is given by [xkcd](https://xkcd.com/color/rgb/) and includes favorites such as 'baby puke green' and a number of brown colors varying from `poo` to `poop brown` and `baby poop brown`. Try it out:" ] }, { @@ -410,7 +598,8 @@ "metadata": {}, "outputs": [], "source": [ - "plt.plot([1, 2, 3], [4, 5, 2], 'xkcd:baby puke green');" + "plt.plot([1, 2, 3], [4, 5, 2], 'xkcd:baby puke green');\n", + "plt.title('xkcd color baby puke green');" ] }, { @@ -448,7 +637,7 @@ "metadata": {}, "source": [ "### Exercise 6, Fill between\n", - "Load the air and sea temperature, as used in Exercise 4, but this time make one plot of temperature vs the number of the month and use the `plt.fill_between` command to fill the space between the curve and the $x$-axis. Specify the `alpha` keyword, which defines the transparancy. Some experimentation will give you a good value for alpha (stay between 0 and 1). Note that you need to specify the color using the `color` keyword argument." + "Load the air and sea temperature, as used in Exercise 4, but this time make one plot of temperature vs the number of the month and use the `plt.fill_between` command to fill the space between the curve and the horizontal axis. Specify the `alpha` keyword, which defines the transparancy. Some experimentation will give you a good value for alpha (stay between 0 and 1). Note that you need to specify the color using the `color` keyword argument." ] }, { @@ -476,7 +665,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Answer to Exercise 1" + "Answer to Exercise 1" ] }, { @@ -494,7 +683,7 @@ "x = 0 \n", "y = a * x ** 2 + b * x + c\n", "print('y evaluated at x = 0 is', y)\n", - "x = 2\n", + "x = 2.1\n", "y = a * x ** 2 + b * x + c\n", "print('y evaluated at x = 2 is', y)" ] @@ -503,7 +692,36 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Back to Exercise 1\n", + "Back to Exercise 1a\n", + "\n", + "Answer to Exercise 1b" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 1\n", + "b = 1\n", + "c = -6\n", + "x = -2\n", + "y = a * x ** 2 + b * x + c\n", + "print(f'y evaluated at x = {x} is {y}')\n", + "x = 0 \n", + "y = a * x ** 2 + b * x + c\n", + "print(f'y evaluated at x = {x} is {y}')\n", + "x = 2.1\n", + "y = a * x ** 2 + b * x + c\n", + "print(f'y evaluated at x = {x} is {y:.2f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Back to Exercise 1b\n", "\n", "Answer to Exercise 2" ] @@ -622,10 +840,8 @@ "sea = np.loadtxt('holland_seawater.dat')\n", "plt.fill_between(range(1, 13), air, color='b', alpha=0.3)\n", "plt.fill_between(range(1, 13), sea, color='r', alpha=0.3)\n", - "plt.xticks(np.linspace(0, 11, 12), ['jan', 'feb', 'mar', 'apr',\\\n", + "plt.xticks(np.arange(1, 13), ['jan', 'feb', 'mar', 'apr',\\\n", " 'may', 'jun', 'jul', 'aug', 'sep', ' oct', 'nov', 'dec'])\n", - "plt.xlim(1, 12)\n", - "plt.ylim(0, 20)\n", "plt.xlabel('Month')\n", "plt.ylabel('Temperature (Celcius)');" ] @@ -655,7 +871,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.8.2" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false }, "varInspector": { "cols": { @@ -692,5 +926,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/notebook1_basics_plotting/py_exploratory_comp_1_sol.ipynb b/notebook1_basics_plotting/py_exploratory_comp_1_sol.ipynb index c5c4663..93f6b71 100644 --- a/notebook1_basics_plotting/py_exploratory_comp_1_sol.ipynb +++ b/notebook1_basics_plotting/py_exploratory_comp_1_sol.ipynb @@ -25,7 +25,18 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "6 * 2" ] @@ -35,7 +46,8 @@ "metadata": {}, "source": [ "Note that the extra spaces are added to make the code more readable. \n", - "`2 * 3` works just as well as `2*3`. But is it highly recommended to use the additional spaces (in fact, it is considered good style to do so).\n", + "`2 * 3` works just as well as `2*3`. And it is considered good style. Use the extra spaces in all your Notebooks.\n", + "\n", "When you are programming, you want to store your values in variables" ] }, @@ -43,7 +55,18 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "a = 6\n", "b = 2\n", @@ -54,14 +77,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Both `a` and `b` are now variables. Each variable has a type. In this case, they are both integers (whole numbers). To write the value of a variable to the screen, use the `print` function (the last statement of a code cell is automatically printed to the screen if it is not stored in a variable, as was shown above)" + "Both `a` and `b` are now variables. Each variable has a type. In this case, they are both integers (whole numbers). To write the value of a variable to the screen, use the `print` function (the last statement of a code cell is automatically printed to the screen if it is not stored in a variable, as was shown above). Note that multiplication of two integers results in an integer, but division of two integers results in a float (a number with decimal places). " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6\n", + "2\n", + "12\n", + "3.0\n" + ] + } + ], "source": [ "print(a)\n", "print(b)\n", @@ -73,14 +107,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can add some text to the `print` function by putting the text between quotes (either single or double quotes work as long as you use the same at the beginning and end), and separate the text string and the variable by a comma" + "You can add some text to the `print` function by putting the text string between quotes (either single or double quotes work as long as you use the same at the beginning and end), and separate the text string and the variable by a comma" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the value of a is 6\n" + ] + } + ], "source": [ "print('the value of a is', a)" ] @@ -97,7 +139,18 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "36" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "a ** b" ] @@ -106,8 +159,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Exercise 1, First Python code\n", - "Compute the value of the polynomial $y=ax^2+bx+c$ at $x=-2$, $x=0$, and $x=2$ using $a=1$, $b=1$, $c=-6$." + "### Exercise 1a, First Python code\n", + "Compute the value of the polynomial $y=ax^2+bx+c$ at $x=-2$, $x=0$, and $x=2.1$ using $a=1$, $b=1$, $c=-6$ and print the results to the screen." ] }, { @@ -121,7 +174,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Answer to Exercise 1" + "Answer to Exercise 1a" ] }, { @@ -136,7 +189,15 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/3 gives 0.3333333333333333\n" + ] + } + ], "source": [ "print('1/3 gives', 1 / 3)" ] @@ -145,7 +206,77 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "(Note for Python 2 users: `1/3` gives zero in Python 2, as the division of two integers returns an integer. Use `1.0/3` instead." + "(Note for Python 2 users (you should really change to Python 3!): `1/3` gives zero in Python 2, as the division of two integers returned an integer in Python 2). The above print statement looks pretty ugly with 16 values of 3 in a row. A better and more readable way to print both text and the value of a variable to the screen is to use what are called f-strings. f-strings allow you to insert the value of a variable anywhere in the text by surrounding it with braces `{}`. The entire text string needs to be between quotes and be preceded by the letter `f`" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 divided by 3 gives 0.3333333333333333\n" + ] + } + ], + "source": [ + "a = 1\n", + "b = 3\n", + "c = a / b\n", + "print(f'{a} divided by {b} gives {c}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The complete syntax between braces is `{variable:width.precision}`. When `width` and `precision` are not specified, Python will use all digits and figure out the width for you. If you want a floating point number with 3 decimals, you specify the number of digits, `3`, followed by the letter `f` for floating point (you can still let Python figure out the width by not specifying it). If you prefer exponent (scientific) notation, replace the `f` by an `e`. The text after the `#` is a comment in the code. Any text on the line after the `#` is ignored by Python. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 divided by 3 gives 0.333\n", + "1 divided by 3 gives 0.333\n", + "1 divided by 3 gives 3.333e-01\n" + ] + } + ], + "source": [ + "print(f'{a} divided by {b} gives {c:.3f}') # three decimal places\n", + "print(f'{a} divided by {b} gives {c:10.3f}') # width 10 and three decimal places\n", + "print(f'{a} divided by {b} gives {c:.3e}') # three decimal places scientific notation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 1b, First Python code using f-strings\n", + "Compute the value of the polynomial $y=ax^2+bx+c$ at $x=-2$, $x=0$, and $x=2.1$ using $a=1$, $b=1$, $c=-6$ and print the results to the screen using f-strings and 2 decimal places." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Answer to Exercise 1b" ] }, { @@ -153,24 +284,26 @@ "metadata": {}, "source": [ "### More on variables\n", - "Once you have created a variable in a Python session, it will remain in memory, so you can use it in other cells as well. For example, the variable `a`, which was defined earlier in this Notebook, still exist. It will be `6` unless you changed it in Exercise 1. " + "Once you have created a variable in a Python session, it will remain in memory, so you can use it in other cells as well. For example, the variables `a` and `b`, which were defined two code cells above in this Notebook, still exist. " ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "a: 6\n" + "the value of a is: 1\n", + "the value of b is: 3\n" ] } ], "source": [ - "print('a:', a)" + "print(f'the value of a is: {a}')\n", + "print(f'the value of b is: {b}')" ] }, { @@ -187,12 +320,12 @@ "metadata": {}, "source": [ "### Basic plotting and a first array\n", - "Plotting is not part of standard Python, but a nice package exist to create pretty graphics (and ugly ones, if you want). A package is a library of functions for a specific set of tasks. There are many Python packages and we will use several of them. The graphics package we use is called `matplotlib`. To be able to use the plotting functions in `matplotlib` we have to import it. We will learn several different ways of importing packages. For now, we import the plotting part of `matplotlib` and call it `plt`. Before we import `matplotlib`, we tell the Jupyter Notebook to show any graphs inside this Notebook and not in a separate window (more on these commands later). " + "Plotting is not part of standard Python, but a nice package exists to create pretty graphics (and ugly ones, if you want). A package is a library of functions for a specific set of tasks. There are many Python packages and we will use several of them. The graphics package we use is called `matplotlib`. To be able to use the plotting functions in `matplotlib`, we have to import it. We will learn several different ways of importing packages. For now, we import the plotting part of `matplotlib` and call it `plt`. Before we import `matplotlib`, we tell the Jupyter Notebook to show any graphs inside this Notebook and not in a separate window using the `%matplotlib inline` command (more on these commands later). " ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -209,27 +342,29 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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Wcc4dAL4GBpe4Kw2IAjCzqkB94Dd/BXHOTXHOxTvn4iMjI09oYJHyMn3xFuYs3cYd53TgzI76/gwkT1zSna7N63HnzGVs3aeCMV/4crZMpJk1KL5dEzgXWF1i2Tzg+uLbVwJfOZ2gKkEkKe0Aj89bxZkdI7l9kArBAk2N8DAmjYql0DnGTlfBmC982XNvDiw0syTgF4qOuc83syfMbFjxmjeBxma2DrgbGFc+44r434HsXMZMTSSiTjVeuaY3VVQIFpBaN67Ni1f1Iiktgyc+XOX1OAGvzFdlOOeSgD6lfPyRo27nAFf5dzSR8ldY6Lhr5jLSs3J47+ZTVQgW4M7v1oz/Pastf/1mA/ExDbmsTyuvRwpYeoWqVGpvfL2Oham7GT+0K72jGng9jvjg3vM70a9NIx6Yk0zqThWMHYvCXSqt79fu4aXP1zCsVwuu69/a63HER1XDqvD6tX2oWyOcMVMTyMrJ83qkgKRwl0ppR8Zhbn9nKW0j6/CsCsGCTpO6NZgwog+b92VznwrGSqVwl0onN7+QsdMSyckrYPKoWBWCBal+bRtz3wWd+HjFTt78fqPX4wQchbtUOs9+nELilgM8f0VP2jdRIVgwG31mW87v2rSo5G2TCsaOpnCXSmV+0nb+8cMmbjg1hot7tfB6HDlJZsZfru5Fq4Y1GTs9UQVjR1G4S6WxLv0g989KIja6AQ8OUSFYqKhXI5w3RsZxIDuP22eoYOxXCnepFLJz87llWgLVw8OYODKWalX1rR9Kuraox1OXdufH9Xt56fNUr8cJCPoOl5DnnOPBOcmsTT/Iq8N707y+CsFC0VXxUQw/JYqJC9fzZcour8fxnMJdQt7URVuYu2w7d53bkTM6qBAslD02rBvdWtTjLhWMKdwltC3feoAnP1zFwE6R3Hp2e6/HkXJWIzyMSSPjABgzLYGcvMpbMKZwl5C1/1Aut0xLJLJudV6+WoVglUV041q8dHVvVmzL5PFKXDCmcJeQVFjouHPmMnZnHeGNkbE0VCFYpXJu16aMGdiOGYu3MDshzetxPKFwl5D0+lfr+GbNbsZf3JVeKgSrlO45ryMD2jbmobnJrN6Z6fU4FU7hLiHn2zW7eeXLNVzauwWj+kV7PY54pGpYFV4b0Yd6NcIZMzWRzEpWMKZwl5Cy/cBh7nhnKR2a1OEZFYJVepF1qzPh2li27MvmvvcqV8GYwl1CRm5+IbdMSyQ3v5BJo+KoVU2FYAJ92zRi3ODOfLJyJ3//rvIUjCncJWQ8syCFZVsP8Ocre9Euso7X40gAuemMNgzu1oznPlnN4o2Vo2BM4S4hYd7y7fzzx03ceFobLurZ3OtxJMCYGS9c1ZPoRrW4dXoi6Vk5Xo9U7hTuEvTWpWcxbnYSca0b8sCQzl6PIwGqbo1wJo2KJTOnqGAsv6DQ65HKlcJdgtqhI/ncPDWRmuFhTLw2lvAwfUvLsXVuVo+nL+3Bzxv28eLna7wep1zpJ0GClnOOB+Yks2H3QV4b0Ydm9Wt4PZIEgSviWjGibzSTvl7P56tCt2BM4S5B698/b2be8u3cfV5HTmsf4fU4EkQevbgr3VvW4+53l7Flb2gWjCncJSgt3bKfJ+evYlDnJtwyUIVgcnx+LRirYhayBWNlhruZRZnZQjNLMbOVZnZHKWsGmlmGmS0rfnukfMYVgX2Hchk7LZGm9Wrw0tW9VAgmJySqUS1evqYXK7dn8ti8lV6P43e+vMojH7jHOZdoZnWBBDP73DlXsm7tO+fcUP+PKPJ/Cgodd7yzlD0Hc5k1ZgANaqkQTE7coM5NGXt2OyYuXE9c64ZcFR/l9Uh+U+aeu3Nuh3Musfh2FpACtCzvwURK89qXa/lu7R4eHdaVnq1UCCYn7+7zOnFqu8Y8PHcFq7aHTsHYcR1zN7MYoA+wqJS7B5jZcjP72My6+WE2kf/ydWo6r321lsv7tOTavioEE/8Iq2K8NqIPDWqFM2ZaAhmHQ6NgzOdwN7M6wGzgTudcyX/eEoHWzrlewOvA3GN8jdFmtsTMluzevftEZ5ZKaNuBw9w5cxkdm9Tl6ctUCCb+FVGnOhOvjWXb/sPc+97ykCgY8ynczSycomCf5pybU/J+51ymc+5g8e0FQLiZ/ebcNOfcFOdcvHMuPjJS17IU3xzJL+CWaYnkFzgmjYqlZrUwr0eSEBQf04hxF3bms1W7mPLtBq/HOWm+nC1jwJtAinPupWOsaVa8DjPrW/x19/pzUKm8nv4oheVbD/CXq3rSVoVgUo7+eHobhvRoxp8/TWXRhuCOMF/23E8DrgMGHXWq4xAzu9nMbi5ecyWwwsyWA68Bw10o/F4jnvtg2Tb+9dNmbjq9DYO7qxBMypeZ8fwVPWndqBa3zlhKembwFoyZVxkcHx/vlixZ4sljS3BYuyuLYRN+oHvLekz/U3/1xkiFSd2ZxaUTf6BHq/pMv6kfVQPoe8/MEpxz8WWtC5yJRY5y8Eg+N09NoHb1MCaoEEwqWKdmdXnm8u4s3riPFz5L9XqcE6KfGAk4zjnGzU5i455DvDaiD03rqRBMKt5lfVoxsl80f/1mA5+t3On1OMdN4S4B558/bmJ+0g7uOb8Tp7ZTIZh455GLu9KzVX3ueW85m/ce8nqc46Jwl4CSsHk/T3+UwjmdmzDmrHZejyOVXPWqRdcJqGLGzVMTg6pgTOEuAWPvwSPcOj2R5g1q8NLVvVUIJgEhqlEtXrmmNyk7MnnkgxVej+MzhbsEhKJCsGXsPZTLpJFx1K8V7vVIIv9xducm3DaoPe8uSePdX7Z6PY5PFO4SEF79Yg3fr9vD48O60b1lfa/HEfmNO8/tyOntIxj/wQpWbMvwepwyKdzFcwtT03ntq3VcEduK4aeETuWqhJawKsarw3vTsFY1bpmWGPAFYwp38VTa/mzumrmMzs3q8tSl3VUIJgGtcZ3qTBwZy/YDh7nn3eUUFgbuC/EV7uKZXwvBCgock0bFqRBMgkJc64Y8dFEXvkjZxV8DuGBM4S6eeXL+KpLSMnjhql60iajt9TgiPrvh1Bgu6tmcFz5dzU/rA7NgTOEunnh/aRpTf97C6DPbMrh7M6/HETkuvxaMtYmozW0BWjCmcJcKl7oziwfmJNM3phH3XdDJ63FETkid6lWZNCqOQ0fyuXX6UvIKCr0e6b8o3KVCZeXkMWZqAnWqhzPh2j4B1bYncrw6Nq3Lc1f0YPGmfbzwaWAVjOknSyqMc477Zyexae8hXh/RhyYqBJMQcEnvllzXvzVTvt3AJysCp2BM4S4V5q0fNrEgeSf3XtCZAe0aez2OiN88PLQLvaIacO97y9m4JzAKxhTuUiGWbNrHswtSOLdLU24+q63X44j4VVHBWB/CwowxUxM4nOt9wZjCXcrdnoNHGDs9kRYNavLi1b30QiUJSa0aFhWMpe7KYvwHK/D6SqMKdylXRYVgS9mfnccbI2OpX1OFYBK6BnZqwm2DOjArIY2ZHheMKdylXL38+Rp+WLeXJy9RIZhUDnec04EzOkTwyLyVnhaMKdyl3Hy1ehcTFq7jqrhWXHNKtNfjiFSIooKxPjSuXY2bpyaQke1NwZjCXcrF1n3Z3DVzOV2a1+PJS7t7PY5IhWpUuxoTR8ayKzOHu99d5knBmMJd/C4nr6gQrNA5Jo+KpUa4CsGk8omNbsjDF3Xly9XpTPpmfYU/vsJd/O7xD1eRvC2DF6/qRevGKgSTyut/BrTm4l4tePGzVH5cv6dCH7vMcDezKDNbaGYpZrbSzO4oZY2Z2Wtmts7MkswstnzGlUA3OyGNGYu38L9nteX8bioEk8rNzHju8h60jazD7TOWsjOj4grGfNlzzwfucc51AfoDY82sa4k1FwIdit9GA5P8OqUEhdU7M3lobjL92jTi3vNVCCYCULt6VSaPiiU7t4BbpydWWMFYmeHunNvhnEssvp0FpAAtSyy7BPiXK/Iz0MDMmvt9WglYmTl5jJmaSN0a4byuQjCR/9K+SV2eu6InSzbv5/mPV1fIYx7XT6CZxQB9gEUl7moJHH3Gfhq//QdAQtT+Q7ncNn0pW/ZlM2FEH5rUVSGYSEnDerXg+gGt+fv3G/k4eUe5P15VXxeaWR1gNnCncy6z5N2lfMpvzv0xs9EUHbYhOlrnPQc75xwLknfy6LwVHMjO47Fh3ejXVoVgIsfy0EVdSdmZxYEKuLi2T+FuZuEUBfs059ycUpakAUdftr4VsL3kIufcFGAKQHx8fOBeWVbKtCszh/FzV/DZql30aFmff93Yj64t6nk9lkhAq1a1Cu/8qT9VqpR/v1KZ4W5FLU9vAinOuZeOsWwecKuZvQP0AzKcc+X/e4dUOOcc7y7ZylMfpZCbX8i4Cztz0+ltdIxdxEcVEezg2577acB1QLKZLSv+2INANIBzbjKwABgCrAOygT/4f1Tx2pa92TzwfhI/rNtL3zaN/nOKl4gEnjLD3Tn3PaUfUz96jQPG+msoCSwFhY5//riJv3yaSlgV46lLu3Nt3+gK2wMRkePn8x9UpXJauyuL+2YnsXTLAQZ2iuSZy3rQokFNr8cSkTIo3KVUufmFTP5mPRO+Wkft6mG8ck1vLundQhfaEAkSCnf5jeVbD3D/7CRW78xiaM/mPDasGxF1qns9logcB4W7/Mfh3AJe+WINf/tuAxF1qjPlujj1w4gEKYW7APDzhr2Mm53Epr3ZjOgbxbgLu+iSeCJBTOFeyWXl5PHcx6uZtmgL0Y1qMf2mfpzaPsLrsUTkJCncK7GvVu/iofdXsCszh5tOb8Pd53ekVjV9S4iEAv0kV0J7Dx7hifmr+GDZdjo0qcMbY06lT3RDr8cSET9SuFcizjk+TNrBY/NWknk4jzvO6cAtZ7ejelVdBk8k1CjcK4mdGTk8PDeZL1LS6dWqPs//qR+dm6noSyRUKdxDnHOOd37ZyjMfpZBXWMhDQ7pw4+ltCFN1gEhIU7iHsM17DzFudjI/bdhL/7aNeO7ynsRE6ILVIpWBwj0EFRQ6/vHDRv7yWSrhVarwzGU9GH5KlIq+RCoRhXuISd1ZVPS1fOsBzunchKcu607z+ir6EqlsFO4hIje/kIkL1/HG1+uoWyOcV4f3ZlgvFX2JVFYK9xCwbOsB7pu1nDW7DnJJ7xY8MrQrjVX0JVKpKdyD2OHcAl78LJW3fthIk7o1ePP6eM7p0tTrsUQkACjcg9SP6/cwbnYyW/Zlc22/aMZd2Jl6NVT0JSJFFO5BJjMnj2cXpDBj8VZaN67FjD/1Z0C7xl6PJSIBRuEeRD5ftYuH5yazO+sIo89sy13ndqRmNVUHiMhvKdyDwJ6DR3hs3krmJ+2gc7O6TLkunl5RDbweS0QCmMI9gDnn+GDZdh7/cCUHj+Rz93kdufmsdlSrWsXr0UQkwCncA9T2A4d5eO4KvlqdTu+oBvz5yp50bFrX67FEJEgo3ANMYaFj+uItPPfxagoKHeOHduWGU2NU9CUix6XMcDezt4ChQLpzrnsp9w8EPgA2Fn9ojnPuCX8OWVls3HOI+2cnsXjjPk5r35hnL+tJdONaXo8lIkHIlz33fwITgH/9zprvnHND/TJRJZRfUMjfv9/Iy5+voVrVKjx/RQ+ujo9SdYCInLAyw905962ZxZT/KJXTqu2Z3D87ieRtGZzXtSlPXdqdpvVqeD2WiAQ5fx1zH2Bmy4HtwP9zzq3009cNWUfyC5jw1Tomfb2eBrXCmXhtLEN6NNPeuoj4hT/CPRFo7Zw7aGZDgLlAh9IWmtloYDRAdHS0Hx46OCVs3s/9s5NYl36Qy/u0ZPzQrjSsXc3rsUQkhJx0uDvnMo+6vcDM3jCzCOfcnlLWTgGmAMTHx7uTfexgk52bzwufpvLPHzfRvF4N/vGHUzi7UxOvxxKREHTS4W5mzYBdzjlnZn2BKsDek54sxHy/dg/j5iSRtv8w1/VvzX2DO1FXRV8iUk58ORVyBjAQiDCzNOBRIBzAOTcZuBIYY2b5wGFguHOu0u2VH0tGdh5PL1jFu0vSaBNRm5mj+9OvrYq+RKR8+XK2zIgy7p9A0amSUsInK3Yy/oMV7DuUy5iB7bjjnA7UCFfRl4iUP71CtRzszioq+vooeQddmtfjretPoUer+l6PJSJ2cTjHAAAIF0lEQVSViMLdj5xzzEncxhPzV3E4t4B7L+jE6DPbEh6moi8RqVgKdz/ZduAwD85J5ps1u4mNLir6at9ERV8i4g2F+0kqLHRMXbSZ5z9ejQMeu7gr1w1Q0ZeIeEvhfhLW7z7IuNlJ/LJpP2d0iOCZy3oQ1UhFXyLiPYX7CcgrKORv323glS/WUqNqFV64sidXxrVSdYCIBAyF+3FasS2D+2cnsXJ7JoO7NeOJS7vRpK6KvkQksCjcfZSTV8DrX61l8jcbaFirGpNGxnJhj+ZejyUiUiqFuw+WbNrHfbOT2LD7EFfEtmL80C40qKWiLxEJXAr333HoSFHR19s/baJF/Zq8fWNfzuoY6fVYIiJlUrgfwzdrdvPgnGS2Zxzm+gEx3HtBJ2pX1/8uEQkOSqsSDmTn8uT8FGYnptE2sjbv/e8A4mMaeT2WiMhxUbgf5ePkHYz/YCX7s3MZe3Y7bhukoi8RCU4KdyA9M4dHPljJJyt30q1FPd6+8RS6tVDRl4gEr0od7s45ZiWk8eT8VeTkF3Lf4E786QwVfYlI8Ku04b51XzYPvp/Md2v3cEpMQ567oiftIut4PZaIiF9UunAvKHT866dNvPBpKgY8eUk3RvZrTRUVfYlICKlU4b4uPYv7ZyeTsHk/Z3WM5OnLutOqoYq+RCT0VIpwzyso5K/frOe1L9dRq3oYL13di8v6tFTRl4iErJAP9xXbMrh3VhIpOzK5qEdzHhvWjci61b0eS0SkXIVsuOfkFfDKF2v523cbaFS7GpNHxTG4ezOvxxIRqRAhGe6LN+5j3OwkNuw5xDXxUTw4pAv1a4V7PZaISIUJqXDPysnjz5+k8u+fN9OqYU2m/rEfp3eI8HosEZEKFzLhvjA1nYfmJLMjM4cbT2vD/7ugI7WqhczmiYgclzLTz8zeAoYC6c657qXcb8CrwBAgG7jBOZfo70GPZf+hXJ6cv4o5S7fRvkkdZt18KnGtG1bUw4uIBCRfdm3/CUwA/nWM+y8EOhS/9QMmFf+3XDnn+Ch5B49+sJKMw3ncPqg9Ywe1p3pVFX2JiJQZ7s65b80s5neWXAL8yznngJ/NrIGZNXfO7fDTjL+xKzOH8XNX8NmqXfRoWZ+pN/WjS/N65fVwIiJBxx8HpVsCW496P634Y+US7gtXp3P7O0vJzS/kgQs788fT21BVRV8iIv/FH+Fe2ss8XakLzUYDowGio6NP6MHaRNQmNrohjw3rRpuI2if0NUREQp0/dnnTgKij3m8FbC9toXNuinMu3jkXHxl5YtcijYmozds39lWwi4j8Dn+E+zzgf6xIfyCjPI+3i4hI2Xw5FXIGMBCIMLM04FEgHMA5NxlYQNFpkOsoOhXyD+U1rIiI+MaXs2VGlHG/A8b6bSIRETlpOs1ERCQEKdxFREKQwl1EJAQp3EVEQpDCXUQkBFnRyS4ePLDZbmDzCX56BLDHj+N4SdsSmEJlW0JlO0Db8qvWzrkyXwXqWbifDDNb4pyL93oOf9C2BKZQ2ZZQ2Q7QthwvHZYREQlBCncRkRAUrOE+xesB/EjbEphCZVtCZTtA23JcgvKYu4iI/L5g3XMXEZHfEdDhbmaDzSzVzNaZ2bhS7q9uZjOL719UxuUAPeXDttxgZrvNbFnx201ezFkWM3vLzNLNbMUx7jcze614O5PMLLaiZ/SVD9sy0MwyjnpOHqnoGX1hZlFmttDMUsxspZndUcqaoHhefNyWYHleapjZYjNbXrwtj5eypvwyzDkXkG9AGLAeaAtUA5YDXUusuQWYXHx7ODDT67lPYltuACZ4PasP23ImEAusOMb9Q4CPKbpCV39gkdczn8S2DATmez2nD9vRHIgtvl0XWFPK91dQPC8+bkuwPC8G1Cm+HQ4sAvqXWFNuGRbIe+59gXXOuQ3OuVzgHYouxn20S4C3i2/PAs4xs9Iu++c1X7YlKDjnvgX2/c6S/1ww3Tn3M9DAzJpXzHTHx4dtCQrOuR3OucTi21lACkXXMT5aUDwvPm5LUCj+f32w+N3w4reSf+QstwwL5HA/1oW3S13jnMsHMoDGFTLd8fFlWwCuKP6VeZaZRZVyfzDwdVuDxYDiX6s/NrNuXg9TluJf6/tQtJd4tKB7Xn5nWyBInhczCzOzZUA68Llz7pjPi78zLJDD3ZcLb/t8cW6P+TLnh0CMc64n8AX/9695sAmW58QXiRS91LsX8Dow1+N5fpeZ1QFmA3c65zJL3l3KpwTs81LGtgTN8+KcK3DO9abo2tJ9zax7iSXl9rwEcrj7cuHt/6wxs6pAfQLz1+wyt8U5t9c5d6T43b8BcRU0m7/5fMH0QOecy/z112rn3AIg3MwiPB6rVGYWTlEYTnPOzSllSdA8L2VtSzA9L79yzh0AvgYGl7ir3DIskMP9F6CDmbUxs2oU/bFhXok184Dri29fCXzliv8yEWDK3JYSxz+HUXSsMRiFzAXTzazZr8c/zawvRT8ve72d6reKZ3wTSHHOvXSMZUHxvPiyLUH0vESaWYPi2zWBc4HVJZaVW4aVeQ1Vrzjn8s3sVuBTis42ecs5t9LMngCWOOfmUfRN8G8zW0fRv3bDvZv42HzcltvNbBiQT9G23ODZwL/DQuiC6T5sy5XAGDPLBw4DwwN05+E04Dogufj4LsCDQDQE3fPiy7YEy/PSHHjbzMIo+gfoXefc/IrKML1CVUQkBAXyYRkRETlBCncRkRCkcBcRCUEKdxGREKRwFxEJQQp3EZEQpHAXEQlBCncRkRD0/wG7CV2sya7oCgAAAABJRU5ErkJggg==\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -241,12 +376,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's try to plot $y$ vs $x$ for $x$ going from $-4$ to $+4$ for the polynomial in the exercise above. To do that, we need to evaluate $y$ at a bunch of points. A sequence of values of the same type is called an array (for example an array of integers or floats). Array functionality is available in the package `numpy`. Let's import `numpy` and call it `np`, so that any function in the `numpy` package may be called as `np.function`. " + "Let's try to plot $y$ vs $x$ for $x$ going from $-4$ to $+4$ for the polynomial\n", + "$y=ax^2+bx+c$ with $a=1$, $b=1$, $c=-6$.\n", + "To do that, we need to evaluate $y$ at a bunch of points. A sequence of values of the same type is called an array (for example an array of integers or floats). Array functionality is available in the package `numpy`. Let's import `numpy` and call it `np`, so that any function in the `numpy` package may be called as `np.function`. " ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -262,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -293,27 +430,29 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -330,16 +469,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that *one hundred* `y` values are computed in the simple line `y = a * x ** 2 + b * x + c`. The text after the `#` is a comment in the code. Any text on the line after the `#` is ignored by Python. Python treats arrays in the same fashion as it treats regular variables when you perform mathematical operations. The math is simply applied to every value in the array (and it runs much faster than when you would do every calculation separately). \n", + "Note that *one hundred* `y` values are computed in the simple line `y = a * x ** 2 + b * x + c`. Python treats arrays in the same fashion as it treats regular variables when you perform mathematical operations. The math is simply applied to every value in the array (and it runs much faster than when you would do every calculation separately). \n", "\n", - "You may wonder what the statement `[]` is (the numbers on your machine may look different). This is actually a handle to the line that is created with the last command in the code block (in this case `plt.plot(x, y)`). Remember: the result of the last line in a code cell is printed to the screen, unless it is stored in a variable. You can tell the Notebook not to print this to the screen by putting a semicolon after the last command in the code block (so type `plot(x, y);`). We will learn later on that it may also be useful to store this handle in a variable." + "You may wonder what the statement like `[]` is (the numbers above on your machine may look different). This is actually a handle to the line that is created with the last command in the code block (in this case `plt.plot(x, y)`). Remember: the result of the last line in a code cell is printed to the screen, unless it is stored in a variable. You can tell the Notebook not to print this to the screen by putting a semicolon after the last command in the code block (so type `plot(x, y);`). We will learn later on that it may also be useful to store this handle in a variable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `plot` function can take many arguments. Looking at the help box of the `plot` function (by typing `plt.plot(` and then shift-tab) gives you a lot of help. Typing `plt.plot?` gives a new scrollable subwindow at the bottom of the notebook, showing the documentation on `plot`. Click the x in the upper right hand corner to close the subwindow again." + "The `plot` function can take many arguments. Looking at the help box of the `plot` function, by typing `plt.plot(` and then shift-tab, gives you a lot of help. Typing `plt.plot?` gives a new scrollable subwindow at the bottom of the notebook, showing the documentation on `plot`. Click the x in the upper right hand corner to close the subwindow again." ] }, { @@ -358,17 +497,19 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -390,6 +531,54 @@ "Names may be added along the axes with the `xlabel` and `ylabel` functions, e.g., `plt.xlabel('this is the x-axis')`. Note that both functions take a string as argument. A title can be added to the figure with the `plt.title` command. Multiple curves can be added to the same figure by giving multiple plotting commands in the same code cell. They are automatically added to the same figure." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### New figure and figure size\n", + "\n", + "Whenever you give a plotting statement in a code cell, a figure with a default size is automatically created, and all subsequent plotting statements in the code cell are added to the same figure. If you want a different size of the figure, you can create a figure first with the desired figure size using the `plt.figure(figsize=(width, height))` syntax. Any subsequent plotting statement in the code cell is then added to the figure. You can even create a second figure (or third or fourth...)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 3))\n", + "plt.plot([1, 2, 3], [2, 4, 3], linewidth=6)\n", + "plt.title('very wide figure')\n", + "plt.figure() # new figure of default size\n", + "plt.plot([1, 2, 3], [1, 3, 1], 'r')\n", + "plt.title('second figure');" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -412,6 +601,110 @@ "Answer to Exercise 2" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Style\n", + "\n", + "As was already mentioned above, good coding style is important. It makes the code easier to read so that it is much easier to find errors and bugs. For example, consider the code below, which recreates the graph we produced earlier (with a wider line), but now there are no additional spaces inserted" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "a=1\n", + "b=1\n", + "c=-6\n", + "x=np.linspace(-4,4,100)\n", + "y=a*x**2+b*x+c#Compute y for all x values\n", + "plt.plot(x,y,linewidth=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The code in the previous code cell is difficult to read. Good style includes at least the following:\n", + "* spaces around every mathematical symbol (`=`, `+`, `-`, `*`, `/`), but not needed around `**`\n", + "* spaces between arguments of a function\n", + "* no spaces around an equal sign for a keyword argument (so `linewidth=3` is correct)\n", + "* one space after every comma\n", + "* one space after each `#`\n", + "* two spaces before a `#` when it follows a Python statement\n", + "* no space between the function name and the list of arguments. So `plt.plot(x, y)` is good style, and `plt.plot (x, y)` is not good style.\n", + "\n", + "These rules are (a very small part of) the official Python style guide called PEP8. When these rules are applied, the code is *much* easier to read, as you can see below:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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U/vSRTdM0JlQUl7j440drveOfJ7Xj1E7NHazIWf4KfAU+F5HlIjK9gvfbA7t8xumebT8hItNFJEVEUjIzM2tdmIhwz6R+hHvmaS7eepDP1u6v9fcaYwLfnKU7vdMwY6PCuevcvlV8Irj5K/BHqOpg3JduZorI6HLvV/TvpwpPs1V1tqomq2pyQoJ/Hnfu3TaeK0/3naa5jvyiEr98tzEmMGXlFvKoT3uVmeN60LZpaE3DLM8vga+qezw/ZwBzgaHldkkHOvqMOwB7/HHs6rptfC+ax0a6izl0jGd92qIaY4LPo59v5MixIgA6t4zl2pFdHa7IebUOfBGJE5H446+Bs4HUcrt9CEzzzNYZBhxR1b21PXZNNIuN4nfn9PGOn/l6K7uy8uqzBGNMPUndfYQ5Pt0w7zmvHzGRoTcNszx/nOG3ARaJyGpgKfCJqs4XkRkiMsOzzzwgDdgCPA/c5Ifj1tilp3Wkf3t334yCYleZnhrGmODgcin3fpDK8bkZY3olcFbf1s4WFSBq3Q9fVdOAQRVsf9bntQIza3us2goPE/44uT8XPbMYgPlr9/Ht5kxG9QyN1qjGhIJ3V6SzYudhACLDhXvPD91pmOUF7ZO2lRnSuTkXDi6dIHTfB2spKLYbuMYEgyN5Rfz10w3e8XWjutE9obGDFQWWkAt8gLvO7VPaZ+dALs8vTHO4ImOMPzy2YCMHPS1U2jWN4Raf9iomRAO/dXwMt5/dyzt+6qstdgPXmAYudfcRXvNpkviHSf2IjQqqVVxrLSQDH+DKYZ3pl+i+gZtf5OKPH61zuCJjzMlyuZQ/vJ/qXbZwVM9WnNu/rbNFBaCQDfyI8DD+/PP+3vEX6/fzxTp7AteYhujNZTtZtct9ozYqPIz7J59iN2orELKBD+4buJcmlz4Pdt+Ha8krLHawImNMTR3IKeBvPjdqZ4yxG7WVCenAB7jz3D7eJ3B3Hz7GE19sdrgiY0xNPPjJeo7mu0/UOreM5aZxdqO2MiEf+C3iorjbp4XyC4u2WQtlYxqI77ce5L2Vu73jP03pb0/UnkDIBz64V64f2rUF4F7V/vfv/YjLZS2UjQlk+UUlzHr/R+/4vIGJjOllD1GeiAU+7hbKD17Qn8hw902eFTsP8+aynVV8yhjjpH99vZW0zFwA4qMjuHdSP4crCnwW+B49Wsdzw+jSRY3/+ukG9h/Nd7AiY0xlNu/P5pmvt3jHd5zbhzZNQrv1cXVY4Pu4+cwedPGsgZudX8x9H6yt4hPGmPrmcil3v/cjRSXuy66DOzXjiqGdqviUAQv8MmIiw3nwwgHe8fy1+/jMZy1MY4zz3ly2k5QdhwCICBMeunAgYWE25746LPDLOaN7qzJz8+/9IJWj+UUOVmSMOW7vkWP8dZ7vnPvu9G4b72BFDYsFfgV+P7EvrRpHA7D/aEGZ7nvGGGeoKn+Ym0p2gXvOfddWcdxszdFqxB8rXnUUka9EZL2IrBWRWyvYZ6yIHBGRVZ4f99b2uHWpaWwk908uveM/54edfL/1oIMVGWM+WrOXLzdkeMd/u2igzbmvIX+c4RcDt6tqX2AY7kXMK5of9a2qJnl+/MkPx61T5w1IZHy/Nt7xne+usbYLxjjkYE4B939YOoniqmGdvc/OmOqrdeCr6l5VXeF5nQ2sB9qf+FOBT0T4y8/7Ex/jbq+6MyuPRz/f5HBVxoSmP360jiyfPvd3TOjtcEUNk1+v4YtIF+BU4IcK3h4uIqtF5FMROcWfx60rbZrEcI/PwxwvfbeNFTsPOViRMaFnfuo+Ply9xzt+4IIBxMdEOlhRw+W3wBeRxsC7wK9VtXwzmhVAZ1UdBDwJvH+C75kuIikikpKZmemv8k7axUM6MKpnKwBU4bfvrCa/yJZENKY+HMot5A/vp3rHFw5uz7g+tiD5yfJL4ItIJO6wf0NV3yv/vqoeVdUcz+t5QKSItKrou1R1tqomq2pyQoLzfTFEhIcuHEBclPvmUFpmLo8tsEs7xtSH+z9ay4GcAgBax0dz36QGcXEgYPljlo4ALwLrVfWxSvZp69kPERnqOW6DmfbSoXksvz+vtKPm89+mkbI9y8GKjAl+n63dxwerSi/lPHThAJrG2qWc2vDHGf4I4CrgTJ9plxNFZIaIzPDs8wsgVURWA/8Epqpqg2pHefnQTj+5tHOs0C7tGFMXDuYUMGtuaSfMCwe356y+bU7wCVMdEsi5m5ycrCkpKU6X4bXn8DHOeXyh98GP/zujC/dPtn9iGuNPqsqNr69gvqetSev4aBbcNsbO7qtJRJaranJF79mTtjXQrlkj7jm/dNbOvxdv57stBxysyJjg8/6q3d6wB/j7LwZa2PuJBX4NXTykA2f5zBL47TurOXLMeu0Y4w97Dh/jXp8utZef3omxvW1Wjr9Y4NeQiPDQRQO86+DuPZLPfR+kVvEpY0xVXC7ljv+uIduzPm2nFrHM8ll+1NSeBf5JaB0fw0M+bZTfX7WHj9fsOcEnjDFVeXnxdhZ5LpGKwKOXDCIuOsLhqoKLBf5JmtA/kYsGd/COZ81NZc/hYw5WZEzDtWHfUf42v7Qr7Q2ju3NaF+uV428W+LVw3+R+tG/WCIAjx4r4zdurKLHFz42pkfyiEm59cxWFxS4A+rdvwm/G93K4quBkgV8LTWIi+cfUJI4vtrMkLYvZC9OcLcqYBubv8zeycX82ADGRYfzj0lOJirBoqgv2u1pLp3Vpwc3jShdhePTzjaxJP+xgRcY0HF9tzOCl77Z5x7Mm9qVH68YOVhTcLPD94JazepLUsRkAxS7l1rdWkVNgvfONOZGM7Hx++/Zq7/jMPq25clhnBysKfhb4fhAZHsYTU5O8Dda2HcjlXpuqaUylXC7l9rdXc9DT4751fDQP/2IgnpZbpo5Y4PtJ55Zx/OWC/t7xeyt28+7ydAcrMiZwPf9tGt9uLp2C+filSbT0rCNt6o4Fvh9dcGqHMlM17/kglbTMHAcrMibwrNh5iIc/2+gd3zimOyN6VNgt3fiZBb6f/WnKKXRrFQdAXmEJN89ZaQumGONxOK+QW+aspNgzfTmpYzNusymY9cYC38/ioiN48vLSaWXr9h7lTx+vc7gqY5ynqvz2nTXs9jyg2CQmgicvO5XIcIuh+mK/03XglHZNucdnwZQ5P+zk/ZW7HazIGOe9uGgbX6zf7x0/cvEgOraIdbCi0GOBX0euHNaZSQMTvePfz/2RLRnZDlZkjHOW78jir5+Wtk64ZkRXzj6lrYMVhSZ/rWk7QUQ2isgWEbmrgvdFRP7peX+NiAz2x3EDmYjw14sGlrmef+PrK8grtPn5JrQcyCngpjdWeK/bD+rQlLvO7eNwVaHJH2vahgNPA+cC/YDLRKRfud3OBXp6fkwHnqntcRuCxtERPH3FYKI91/M3Z+Rw17s/EsirjBnjT8UlLm6Zs5L9R90LkTeLjeTpKwZb6wSH+ON3fSiwRVXTVLUQeAuYUm6fKcCr6rYEaCYiieW/KBj1TWzCX35eOj//w9V7ePm77c4VZEw9emzBJr5POwi459v/49IkOjS36/ZO8Ufgtwd2+YzTPdtqug8AIjJdRFJEJCUzM9MP5Tnv4uSOXH56J+/4wXnrWboty8GKjKl781P38q+vt3rHt57V01avcpg/Ar+iZ6HLX7Oozj7ujaqzVTVZVZMTEhJqXVyguO/8fgzq0BRw99uZOWcF+4/mO1yVMXVj8/5sbvfpkzO6VwK/OrOngxUZ8E/gpwMdfcYdgPLLP1Vnn6AWHRHOv64cQou4KAAyswu44bXl9lCWCTpHjhUx/bXl5Ba6/2x3ahHLP6cmERZmfXKc5o/AXwb0FJGuIhIFTAU+LLfPh8A0z2ydYcARVd3rh2M3KO2bNeKpy04l3PMHf9Wuw9zzfqrdxDVBw+VSfvOfVWw7kAtAo8hwnrtqCM1ioxyuzIAfAl9Vi4Gbgc+A9cDbqrpWRGaIyAzPbvOANGAL8DxwU22P21Cd0aNVmYWZ31meziuLtztXkDF+9MjnG/lyQ4Z3/PDFA+mb2MTBiowvv6wQrKrzcIe677ZnfV4rMNMfxwoGvxzRhbV7jvLuCnc3zT9/sp4ereMZ2dMaSJmG6/2Vu8vcpL1hTDcmDWznYEWmPJsM6wAR4YEL+jPIs2hKiUu56Y3lbLXOmqaBWrnzEHe8u8Y7PrNPa+44xx6uCjQW+A6JiQxn9lVDaNPE3QP8aH4x172SwuG8QocrM6Zm9h45xg2vLfcuQt6zdWOemJrkvVdlAocFvoPaNInh+WnJxES6/zNsO5DLTW+soKjE5XBlxlRPTkExv3x5GRnZpU/SvnB1MvExkQ5XZipige+wgR2a8dglSd7x4q0H+cNcm7ljAp+7bcIKNuxzNwWMCBP+dcVgOreMc7gyUxkL/AAwcUAit/ssAvGflF1lbn4ZE4j+/PE6vtpY+jT8gxcO4IzuNvEgkFngB4ibz+xRZnnEhz/byAerrIe+CUwvfJvGK9/v8I5vGtudS5I7nuATJhBY4AcIEeGhCwdwRveW3m2/e2cN32896GBVxvzUR6v38JdP1nvH5w1I5Ldn93awIlNdFvgBJCoijGeuHELP1o0BKCxxMf3VFNbvPepwZca4LUk7WKZHzpDOzXn0kkHWNqGBsMAPME0bRfLyL0+jdbx7umZ2QTFXv7SU9EN5DldmQt3GfdlMfzWFQs8ssm4JcbwwLZmYyHCHKzPVZYEfgDo0j+WVa4YSH+1+EDoju4BpLy0lK9fm6Btn7MrK46oXf+BovnvFtoT4aF755VCax1mPnIbEAj9A9U1swuxpyUSFu/8TpWXm8n8vLyU7v8jhykyoycwu4MoXf/DOtW8cHcHL/3eaLUDeAFngB7Dh3Vvyj6lJiOfy6Jr0I1z/aoq1VDb15mh+EVe/tJQdB92XFKMiwnh+WjL92zd1uDJzMizwA9zEAYk88PMB3vGStCxunrPSnsY1dS7X8xTtOs+kgTCBJy87leE+M8lMw2KB3wBcfnon7pxQ2ojqi/X7+c3bqylx2dO4pm7kF5Vw/aspLN9xyLvtrxcO5JxT2jpYlaktC/wG4sax3Zkxprt3/NHqPdzx3zW4LPSNnxUWu7jx9eUs9nkG5N5J/bjkNHuwqqGrVT98EXkYOB8oBLYCv1TVwxXstx3IBkqAYlVNrs1xQ9WdE3qTV1jMq54nHN9dkU5URBgPXtAfEZsHbWqvsNjFLW+uKNMy4Xfn9OaakV0drMr4S23P8BcA/VV1ILAJuPsE+45T1SQL+5MnItx//ilM9TnTenPpTu79YK2d6ZtaKypxh/1na/d7t80c152Z43o4WJXxp1oFvqp+7lniEGAJ7sXJTR0KCxMevGAAF57a3rvttSU7+MMHqRb65qQVlbi4eU7ZsL9+VFdrmRBk/HkN/xrg00reU+BzEVkuItNP9CUiMl1EUkQkJTMz80S7hqywMOHvvxjIlKTS5ePm/LCTu9/70ULf1FhBcQkz3ygb9teN7MrvJ/a1S4VBpspr+CLyBVDRrflZqvqBZ59ZQDHwRiVfM0JV94hIa2CBiGxQ1YUV7aiqs4HZAMnJyZZelYgID+OxS5IIE2HuSndXzf+k7KLI5eLvFw0kItzux5uq5ReVcMNry/lmU+nJ1XUjuzLrPAv7YFRl4Kvqz070vohcDUwCztJKVu1Q1T2enzNEZC4wFKgw8E31hYcJj1w8iDAR74Lo763YTV5BCU9clkR0hPU4MZXLLXAvq/l9WulsnOmju3H3uX0s7INUrU4DRWQCcCcwWVUr7O4lInEiEn/8NXA2kFqb45pS4Z7LO743cuev3cf1ry7nWKE9kWsqdii3kCte+KFM2N96Vk8L+yBX23/3PwXE475Ms0pEngUQkXYiMs+zTxtgkYisBpYCn6jq/Foe1/gID3P30r/WZ+rcwk2ZXPXiD7YouvmJvUeOcfFz37NqV+kM6jsn9OG28b0s7IOcBPLaqcnJyZqSkuJ0GQ2GqvKPLzbzxJebvdt6tm7Mq9cOJbFpIwcrM4Fia2YO015cyu7DxwAQgT9NPoWrhndxtjDjNyKyvLLp73ZnL4iICLeN78U9k/p5t23OyOGify1m8/5sByszgWD5jix+8cxib9hHhH9Ow34AAA1vSURBVAlPTD3Vwj6EWOAHoWtHduWJqUlEhrv/eb7nSD4XPbOYxVsPOFyZccr81L1c/vwPHMpzt9duFBnOC1cnM3lQuyo+aYKJBX6QmpLUnhevPo3YKPdMnaP57pWz3l2e7nBlpj6pKi8u2saNb6ygoNjdYbVlXBRzrj+dsb1bO1ydqW8W+EFsdK8E3r5huHe5xKIS5fZ3VvPY5xvtAa0QUFTi4vdzU/nzx+s4fquua6s43rvpDE7t1NzZ4owjLPCDXP/2TZk7cwR92sZ7t/3zf1u46Y0V5BYUn+CTpiE7nFfItBeX8ubSnd5tgzs1490bz6BzyzgHKzNOssAPAe2bNeKdGcMZ3SvBu23+2n1c9MxidmXZ4ujBZsO+o0x5+rsyc+x/ntSOOdcPo4WtQRvSLPBDRHxMJC9dncw1I0rn6m/Yl83kpxaxaLPdzA0WH6/ZwwVPL/YuSQju9saPX5pETKQ9eR3qLPBDSER4GPee34+/XzTQO4PnUF4R0176gae/2mLX9RuwohIXD85bz81zVnLMs+ZxbFQ4z145mJnjetgDVQawwA9Jl5zWkTevH+a9metSePizjUx/LcWezG2A9hw+xtTZS5i9MM27rWurON6fOYIJ/RMdrMwEGgv8EJXcpQUf/2okQ7u28G77Yn0GE5/4lmXbsxyszNTEVxszOO+f35ZZe/asPq15f+YIerWJP8EnTSiywA9hreNjeOO607nOpwfPniP5TJ29hCe/3GyLpAew/KIS/vjRWn758jLvw1ThYcIdE3rz/LRkmjaKdLhCE4isl44BYMG6/fzuv6s57AkPgOTOzXnskiQ6tYx1sDJT3sZ92dz61ko27Cttl9GmSTRPXja4zL/YTGiyXjqmSuP7tWHer0aR3Ln0gZyUHYc494mF/GfZTgL5xCBUlLiUZ7/ZyvlPLSoT9mf1ac0nvxplYW+qZGf4poziEhf/+norT5S7pDO6VwIPXTiA9s2s66YTtmbm8Nt3VrNyZ2lL4+iIMP5wXl+uHNbZZuEYrxOd4Vvgmwqt3nWY295eRVpmrndbXFQ4d03syxVDOxEWZgFTHwqLXTz3zVae/GoLhZ5eOAAD2jfl0UsG2Y1Z8xN1dklHRO4Xkd2exU9WicjESvabICIbRWSLiNxVm2Oa+jGoYzM+uWUU147syvGTx9zCEu55P5ULn1nM2j1HnC0wBKRsz+K8f37Lows2ecM+Mly4fXwv3rvpDAt7U2O1OsMXkfuBHFV95AT7hAObgPFAOrAMuExV11X1/XaGHxiW7zjEHf9dzVafs/0wgavP6MKvf9bLZoT4WUZ2Pn/7dKN3neLjBrRvyt8uGki/dk0cqsw0BE7ftB0KbFHVNFUtBN4CptTDcY2fDOncnE9+NYpfndmDqHD3HxmXwsvfbWfcI1/z+pIdFJe4qvgWU5WC4hKeX5jGmY98UybsY6PCuXdSP96fOcLC3tSKPwL/ZhFZIyIviUhFPVfbA7t8xumebaYBiYkM5zdn9+bTX4/ijO4tvduzcgv5w/upTHpyEV9tyLDZPCfB5VI+WLWbsx79hgfmrSfHp4vpOae0YcFvxnDNyK6E230TU0tVXtIRkS+AthW8NQtYAhwAFPgzkKiq15T7/MXAOap6nWd8FTBUVW+p5HjTgekAnTp1GrJjx44a/YJM3VNVPvlxLw/N2+BdLu+407o0584JfUjuYlMEq6KqfL0xk8cWbOLH3WXviXRPiOP+yacwqmdCJZ82pmL1MktHRLoAH6tq/3LbhwP3q+o5nvHdAKr6UFXfadfwA1t+UQkvfJvGv77eSl5hSZn3RvVsxS1n9rS54RVQVRZuPsDjCzaxatfhMu81j43kljN7ctXwzkSG22MypubqLPBFJFFV93pe3wacrqpTy+0Tgfum7VnAbtw3bS9X1bVVfb8FfsOQmV3AU//bzJylOykqKfvn6fSuLZgxtjtjeyWE/FzxEpfyaepenv1mK6m7j5Z5LzoijGtGdmXGmO52E9zUSl0G/mtAEu5LOtuBG1R1r4i0A15Q1Yme/SYC/wDCgZdU9YHqfL8FfsOy82Ae//hiE++v2k35Njw9WzfmulFdmZLUPuT6sh85VsR/l6fz6vfby/SpB4gKD+Py0ztx49jutGkS40yBJqjYg1emXm0/kMvTX23hvZW7f9KArUlMBL8Y0pHLT+9Ej9aNHaqw7qkqqbuP8uayncxdsdvbo/646IgwLknuyE3jupPY1J5eNv5jgW8csSsrj5e/285/lu0kt9w1fnCvsXrB4A6cPzCRZrHBsfReRnY+H63eyzspu8r0uzmuaaNIrh7emWlndKFV42gHKjTBzgLfOOpofhFvLd3JGz/s/MklDXA/PTqyRyvO7Z/I+H5taN7A1l3NyM5nwbr9fLx6Lz9sO/iTy1kAfdrGM214F35+ajtioyLqv0gTMizwTUBwuZTvth7g9SU7+HJ9BsUVJGN4mDCkU3PG9E5gTK8E+iU2Cbi+PUUlLtakH2HR5gP8b8N+VqdX3GYiJjKMif0TufS0jgzt2iLkb1qb+mGBbwLOwZwCPl6zl/dWpFcamOC+BHJalxac3rUFp3ZqRr92Ter9DDk7v4g16UdYteswKduzWLotq8JLVAAiMLRLC6YktWfSoESaxNiMG1O/LPBNQNt5MI/5a/fyaeq+Mu1/KxIm0D2hMb3bxtOjdWO6JzSmc8tY2jVrRMu4qJM+i1ZVDuQUsjMrj11ZeaQdyGXjvqNs2p/D9oO5nOivSXiYcFqX5pxzSlsmDki02TbGURb4psE4kFPAt5sz+XpjJos2H+BgbvUXVY+OCKNV42iax0XSPDaK+JgIoiPCiYkMIzxMcKk72AuKXOQWFpNbUMLhY4UcyC7kYG7BT54hOJH2zRoxrFtLxvZOYHSvBJs7bwKGBb5pkFSVtAO5LN2WxbLtWaTuPsKWjJwKb4rWpTCB3m2bkNSxGad2bMawbi3p2KKRXZM3AelEgW/TBUzAEhG6J7gv21w2tBMAeYXFrN+bzdaMHLZk5rA1I4fdh4+x5/AxjuYXV/GNJ9YkJoKOLWLp5PnRq02899JRqD0sZoKTBb5pUGKjIhjSuTlDOv+0MWtOQTFZOYVk5RVyKK+Q3IJiCopc5BeX4FL3mXqYCJHhYTSODic2KoL4mAhaNY4mIT7aQt0EPQt8EzQaR0fQODqCTi1jnS7FmIBk7fiMMSZEWOAbY0yIsMA3xpgQYYFvjDEhwgLfGGNChAW+McaECAt8Y4wJEQHdWkFEMoEdJ/nxVsABP5bjL1ZXzVhdNWN11Uww1tVZVRMqeiOgA782RCSlsn4STrK6asbqqhmrq2ZCrS67pGOMMSHCAt8YY0JEMAf+bKcLqITVVTNWV81YXTUTUnUF7TV8Y4wxZQXzGb4xxhgfFvjGGBMiQiLwReS3IqIi0srpWgBE5M8iskZEVonI5yLSzumaAETkYRHZ4Kltrog0c7omABG5WETWiohLRBydQiciE0Rko4hsEZG7nKzFl4i8JCIZIpLqdC2+RKSjiHwlIus9/w1vdbomABGJEZGlIrLaU9cfna7pOBEJF5GVIvKxv7876ANfRDoC44GdTtfi42FVHaiqScDHwL1OF+SxAOivqgOBTcDdDtdzXCpwIbDQySJEJBx4GjgX6AdcJiL9nKzJx7+BCU4XUYFi4HZV7QsMA2YGyO9ZAXCmqg4CkoAJIjLM4ZqOuxVYXxdfHPSBDzwO3AEEzN1pVT3qM4wjQGpT1c9V9fjCsEuADk7Wc5yqrlfVjU7XAQwFtqhqmqoWAm8BUxyuCQBVXQhkOV1Heaq6V1VXeF5n4w6y9s5WBeqW4xlGen44/vdQRDoA5wEv1MX3B3Xgi8hkYLeqrna6lvJE5AER2QVcQeCc4fu6BvjU6SICTHtgl884nQAIr4ZCRLoApwI/OFuJm+fSySogA1igqoFQ1z9wn6C66uLLG/yatiLyBdC2grdmAb8Hzq7fitxOVJeqfqCqs4BZInI3cDNwXyDU5dlnFu5/ir9RHzVVt64AIBVsc/yssCEQkcbAu8Cvy/0L1zGqWgIkee5VzRWR/qrq2D0QEZkEZKjqchEZWxfHaPCBr6o/q2i7iAwAugKrRQTclydWiMhQVd3nVF0VmAN8Qj0FflV1icjVwCTgLK3HhzRq8PvlpHSgo8+4A7DHoVoaDBGJxB32b6jqe07XU56qHhaRr3HfA3HypvcIYLKITARigCYi8rqqXumvAwTtJR1V/VFVW6tqF1Xtgvsv6+D6CPuqiEhPn+FkYINTtfgSkQnAncBkVc1zup4AtAzoKSJdRSQKmAp86HBNAU3cZ1svAutV9TGn6zlORBKOz0ITkUbAz3D476Gq3q2qHTx5NRX4nz/DHoI48APcX0UkVUTW4L7kFBBT1YCngHhggWfK6LNOFwQgIheISDowHPhERD5zog7PDe2bgc9w33x8W1XXOlFLeSLyJvA90FtE0kXkWqdr8hgBXAWc6fkztcpzBuu0ROArz9/BZbiv4ft9GmSgsdYKxhgTIuwM3xhjQoQFvjHGhAgLfGOMCREW+MYYEyIs8I0xJkRY4BtjTIiwwDfGmBDx/7kBf7niKkVeAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "a = 1\n", + "b = 1\n", + "c = -6\n", + "x = np.linspace(-4, 4, 100)\n", + "y = a * x**2 + b * x + c # Compute y for all x values\n", + "plt.plot(x, y, linewidth=3);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use correct style in all other exercises and all Notebooks to come. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 2b. First graph revisited\n", + "Go back to your Exercise 2 and apply correct style. " + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -448,7 +741,7 @@ "metadata": {}, "source": [ "### Exercise 4, Subplots and fancy tick markers\n", - "Load the average monthly air temperature and seawater temperature for Holland. Create one plot with two graphs above each other using the subplot command (use `plt.subplot?`). On the top graph, plot the air and sea temperature. Label the ticks on the horizontal axis as 'jan', 'feb', 'mar', etc., rather than 0,1,2,etc. Use `plt.xticks?` to find out how. In the bottom graph, plot the difference between the air and seawater temperature. Add legends, axes labels, the whole shebang." + "Load the average monthly air temperature and seawater temperature for Holland. Create one plot with two graphs above each other using the `subplot` command (use `plt.subplot?` to find out how). On the top graph, plot the air and sea temperature. Label the ticks on the horizontal axis as 'jan', 'feb', 'mar', etc., rather than numbers. Use `plt.xticks?` to find out how. In the bottom graph, plot the difference between the air and seawater temperature. Add legends, axes labels, the whole shebang." ] }, { @@ -470,27 +763,97 @@ "metadata": {}, "source": [ "### Colors\n", - "There are five different ways to specify colors in matplotlib plotting; you may read about it [here](http://matplotlib.org/examples/pylab_examples/color_demo.html). A useful way is to use the html color names. The html codes may be found, for example, [here](http://en.wikipedia.org/wiki/Web_colors). But the coolest way is probably to use the xkcd names, which need to be prefaced by the `xkcd:`. The xkcd list of color names is given by [xkcd](https://xkcd.com/color/rgb/) and includes favorites such as 'baby puke green' and a number of brown colors vary from `poo` to `poop brown` and `baby poop brown`. Try it out:" + "If you don't specify a color for a plotting statement, `matplotlib` will use its default colors. The first three default colors are special shades of blue, orange and green. The names of the default colors are a capital `C` followed by the number, starting with number `0`. For example" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot([0, 1], [0, 1], 'C0')\n", + "plt.plot([0, 1], [1, 2], 'C1')\n", + "plt.plot([0, 1], [2, 3], 'C2')\n", + "plt.legend(['default blue', 'default orange', 'default green']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are five different ways to specify your own colors in matplotlib plotting; you may read about them [here](http://matplotlib.org/examples/pylab_examples/color_demo.html). A useful way is to use the html color names. The html codes may be found, for example, [here](http://en.wikipedia.org/wiki/Web_colors). " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "plt.plot([1, 2, 3], [4, 5, 2], 'xkcd:baby puke green');" + "color1 = 'fuchsia'\n", + "color2 = 'lime'\n", + "color3 = 'DodgerBlue'\n", + "plt.plot([0, 1], [0, 1], color1)\n", + "plt.plot([0, 1], [1, 2], color2)\n", + "plt.plot([0, 1], [2, 3], color3)\n", + "plt.legend([color1, color2, color3]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The coolest (and nerdiest) way is probably to use the xkcd names, which need to be prefaced by the `xkcd:`. The xkcd list of color names is given by [xkcd](https://xkcd.com/color/rgb/) and includes favorites such as 'baby puke green' and a number of brown colors varying from `poo` to `poop brown` and `baby poop brown`. Try it out:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot([1, 2, 3], [4, 5, 2], 'xkcd:baby puke green');\n", + "plt.title('xkcd color baby puke green');" ] }, { @@ -528,7 +891,7 @@ "metadata": {}, "source": [ "### Exercise 6, Fill between\n", - "Load the air and sea temperature, as used in Exercise 4, but this time make one plot of temperature vs the number of the month and use the `plt.fill_between` command to fill the space between the curve and the $x$-axis. Specify the `alpha` keyword, which defines the transparancy. Some experimentation will give you a good value for alpha (stay between 0 and 1). Note that you need to specify the color using the `color` keyword argument." + "Load the air and sea temperature, as used in Exercise 4, but this time make one plot of temperature vs the number of the month and use the `plt.fill_between` command to fill the space between the curve and the horizontal axis. Specify the `alpha` keyword, which defines the transparancy. Some experimentation will give you a good value for alpha (stay between 0 and 1). Note that you need to specify the color using the `color` keyword argument." ] }, { @@ -556,12 +919,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Answer to Exercise 1" + "Answer to Exercise 1" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -570,7 +933,7 @@ "text": [ "y evaluated at x = -2 is -4\n", "y evaluated at x = 0 is -6\n", - "y evaluated at x = 2 is 0\n" + "y evaluated at x = 2 is 0.5099999999999998\n" ] } ], @@ -584,7 +947,7 @@ "x = 0 \n", "y = a * x ** 2 + b * x + c\n", "print('y evaluated at x = 0 is', y)\n", - "x = 2\n", + "x = 2.1\n", "y = a * x ** 2 + b * x + c\n", "print('y evaluated at x = 2 is', y)" ] @@ -593,24 +956,65 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Back to Exercise 1\n", + "Back to Exercise 1a\n", + "\n", + "Answer to Exercise 1b" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y evaluated at x = -2 is -4\n", + "y evaluated at x = 0 is -6\n", + "y evaluated at x = 2.1 is 0.51\n" + ] + } + ], + "source": [ + "a = 1\n", + "b = 1\n", + "c = -6\n", + "x = -2\n", + "y = a * x ** 2 + b * x + c\n", + "print(f'y evaluated at x = {x} is {y}')\n", + "x = 0 \n", + "y = a * x ** 2 + b * x + c\n", + "print(f'y evaluated at x = {x} is {y}')\n", + "x = 2.1\n", + "y = a * x ** 2 + b * x + c\n", + "print(f'y evaluated at x = {x} is {y:.2f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Back to Exercise 1b\n", "\n", "Answer to Exercise 2" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -635,17 +1039,19 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -673,17 +1079,19 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -716,12 +1124,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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n/Kwd8YWxR+yupVIj8yNiXK1R/SPr3vjjPQF/uMcmnbEWSvXVaWcjqepWVX0Wa4poXxFphtVAqwmQJSLbvee8pFZVRzgTyKH3GITfNKjEwvg+94yftT3uaEyh3bWcIrkwHJrxVEMLl7w1p0FsjDuaVnm+z9fHqlW1ENgrIuMARKQpMBFYJCKTReT4v/kegBs4hDVEMVFVO6tqZ6yxZBPIRmjMTckeC4y1u46qJB6J73PP+Eu2hV0oNynrZHcJ1bn/ly+u7tlte9jX6admwF1+bH8lcJ93xtH/gAdVdSvWb4THZyL9E5gNdMDqzbz0+M6qmgUUiMjgANUfccwsixCam5L9HRBuDWZOcbRRyZrHvn67e2lSefg0Me82bjfb6gV6OlZAdGm3e+m2b6ZG4hQ3XxQDXUnWiH5iMlKYK+QQmZuSPZkICGOAeocT+t49YdYPscUxRXbX8qPR+XvsLqEyDod737J3rjzD7jqCKBG43e4i6goTyCEwNyVbgIftrsMf9Q4l9Lt7wsxNsSXOYrtrAWBM3lG7SziV6r/n3rerRdNDp12ENgr8gkxpaXcRdYEJ5NCYhPVYdUSpfzCx/10TZm2MOeYssbsWBh2Mt7uEk40akL7wkklfnW13HSGQhNU4ywgyE8ihcYvdBdRUgwOJqXedO3O97aHcqTisrtAS4o9t+eLFXw+yu44QuoFMaW53EdHOBHKQzU3JPgtr7mXEapiXdPZd585c5yx1HLOtiHhPZ+Ld9p3/BFr67Ws/dyfGlyZUv23UqAf8xu4iop0J5OCLin/EDfOSBtw1ceZaZ6nDnnaYgpPUw1m2nPskP5vx4ZIhfdedaXcdNrjarFQdXCaQg2huSnYrrEdFo0Kj/fUG3jnp4gzbQnlsnu3N9Zs3Obj6+QcfiYSG88HQFqvntxEkJpCD6xdA2N2Mqo3GOfUH3TH54gxHmZSF/OSj88pDfs4KBD284t3LWzsdWpf/v5ljdwHRrC7/wwoq71S3OXbXEQxN9tYfdMfki9NDHsp9jtjakOlPd/15fed2OW3srCEMXECmRMCj7JHJBHLwjMJqlhKVmu5pMPj282escpRL6K5aWxzrELJznaR3962Lb7nqjWF2nT+MJFDH+00Ekwnk4LnK7gKCrdmuhkNuO3/GipCFspMWtCrJDcm5Kohxlu9a/MbVIVn3MELMsbuAaGUCOQjmpmQnATPsriMUmmc3HPrbC6avELe4Q3LCkQd2heQ8P1LPh8/cmt+owdGGoT1vWBtMptTFWSZBZwI5OKYRJg3oQ6HFjkZDf3vhRctCEspjc0PaSH9K2sIFk0cv7hvKc0aIOXYXEI1MIAdH1Ex181XLrMbDbr1o2lLx4AnqiYYeDNk82PpJRRv/89fbh4fqfBHmCjLF5EeAmW9ogM1NyW5AhD+ZV1OttjYZ/puLpi0Jaih3Oxqix3e16Ls35yTExZbHhuZ8EacdZk5ywJlADryJRNncY3+02dJ0+M0zLvwuaKFc390ZpwZ9aOS2a/65ss8ZW+v8svTVmG13AdHGNKgPsIPfdn5yz+az+6V/PqddzpZ+Peyuxy67k/MX/eXdD4cjQViuqt/obaxp1DXgx/Vq32rfiux5kwYG6/hRZDfJ2t7uIqKJCeRAyhQB9gEtADxu5649m/tvW/3FVUk/LJvYx10eX6eunHf1zl3417c+HhHwUL45ZQl/7RqUZv8O8eTunj+R1i3yWwTj+FGoO8m61e4iooUJ5EDKlEHAssreUqXw8P4O69fPm1GW8fXsM4sOtawT/8PvTMld+PSbAQ7lD1vPZ9qg0QE7XgUvP+pafvW0T+pSW83auoZkfcXuIqKFCeRAypT7gIeq20wVT2lRgw1bVp2Tn/7Z1W1ytvSL5iWA2N5334K///vTUQE74I7E5XQeH/DQHHzW9wuXvj2nrjYOqqnXSNY5dhcRLUwgB1KmfAGc6+9uHrdz154f+m/L+OLKpM1Lz4vKoY2s/jkLnv3n/wUmlMtkN3HnB3TB07jY0qwDy8a0qpdYkhTI49YBWSRr0Mbz6xoTyIFizck8ADSqzWG8Qxvr1s+fXr7mq9lnHj3UKmqGNrYO2Dv/+Vc/C8xQQ6PzCiiIDdDTc1q24J/Xbhk5IKNnYI5X53QkWbPtLiIamEAOlEzpA6wN5CFV0dLi+hu2rjwnN/3zq9vu/aF/xA9tbBm0Z/4LL39e+1CeOOR7vmwZkP4Ss8//bP6/nrg/KGPSdcTlJOu/7S4iGkRdIItIoarW9/59EvAXYJyq7hSRnwO3ejctAG5V1UXebecBbYASoBS4TlUzfD5xplwPPBeor6MyHrdz994f+m1d/cVVET208cOQ3fP/8eIXtQvAJ7ot5M7etR7vbdLw8NrcJeNS6niP49p6gWS93u4iokGM3QUEi4iMA/4GTPCG8RTgemCEquaJSCrwoYgMUtUc726zVXWliFwNzMW/J5GC/oitw+lu1y55Vbt2yatQ5WhBbvuMdfNmlK75anZyJA1t9FjabvTPrj933kvPf5lW44OMPBCASrRg+TtXNjVhXGvmt4sAicorZOA84DVgkqpmel9fCPxOVf9XYduHAFT1fu8V8m3eQE4G3lfVXj6fOFN+ALoH7Avxw49DG6vG5aZ/dk2bvT/0j4hOXBtH7Zz3yt+/TqvRzgdjv6fpebUasnjkN88svuf6l02visBoTbLus7uISBeNgVwGHAHSVHVthdcPAF1U9XCF1y4ArlLVi04K5N8ALVX1Hp9OmikNsIZAwoLH7dy9d0u/rRlfXJG0acnksB7aWJ+2Y95rT3+T5veOSgGOqTW+qdej044lm7+4KCgPl9RRF5Os79ldRKSLxkAuAv4HbFXVmyu8XlkgXwhcoarTK4wh1wOcQKqq7vXppJkyGFgasC8igLxDG+vCeWjj+3O2z/vnU/9N83vH7uN2s7We39PfnA733n2Lz0ls1rjALEUUOA+QrNXOwTdOLxrHzjzATGCgiFS8wt0AnH3Stqne14+bDXQB3gCe8eOcvWtQZ0iIUK9Ry12Dh898auQv/zGw+U2v91o/+eYb57XpsXqT3bUd1+ebzmmzfztmnt87js7b7f/ZVN956s4cE8YB18nuAqJBVN7UU9Ui7028hSKyT1VfAp4A/iAiE1U1X0T6YTXZHnzSvmUich+wVUR6qupGH04ZEfNXRZD4pMLevUZ+RK+RH+FxO/fs3dJ3S8YXV9o+tNH3y65p6tB5b8ydl+bzTmPzinnZvxwYP2zZgovGf2tuQgWeCeQAiMpABlDVAyIyEVggInmq+pGItAO+ExHFGme+vLJhCVUtFpE/AbcBP/PhdBE5P9jhdLdtd2Z623ZnpjPppt8cLchtl7F+/ozSNV/NPrPwYOuWoa6n3+fd0twxOu/tx+an+bTDwENx/hw/MaFk8yfP3mzGjYPDBHIARN0Ysi0yZQMRcpXsC++sjY3b0sfuX/XZ1W32bj47pLM2Vly4ed67Dy9Mq3bDEsc2Eqf4+Niulqx494rsASkb62xL1CArAZJINoFSGyaQAyFTirGWR49KHrdzT86WvltXf3lFwqYlk/u4yxKC/rUuv2jTvPd+vyjttBspbpIml1HirLaeX89+e/7f7nvCDFUEl5n6VksmkGsrUxoDB+0uI1SsWRvt1q1fML1szZeXnxHMoY0lMzfO/+CB704foiOHZ7KoWfLpNmndPG/V7gXnpjqC0SzfqGgwybrc7iIimQnk2sqUM4CwmbEQStbQRr2N29LHBW1o47tLNsz/8L4lVYfyA2cu4qEzR1T1tojnwM7/TS5r33p/q0DXZpxiJsn6rt1FRLKovakXQnX2f3Rr1sbRXj1HfNyr54iP8bide3O2nvXD6i+uTAzU0Mawt3qN9sR45n9817LKQ3lU/mnX13vm/j9sbt96/5Da1mH4xNzYqyVzhVxbmTIDMFcFJ1GlqCCv3boN8y86lvHlFbWetbHgyu8XfHrH8lP7Ke+LW03rif0r26dfz8xFq9+fXeXVsxFwT5OsN9pdRCQzgVxbmXID/j1EUuf8NLQxNjf986tb7dk04LRjvpUeA9X513y/8LNbV5wYyh7ycE5tfvL2sTFlO/KXjm3WoF5R/VqUbvjnE5J1qt1FRDIzZFF7dXbIwlc/DW18Qs8Rnxwf2tiS8eUVCZnfTfFpaEMQGf1yn5Eehy784jcrf2q76aA5bUpy2ZtQ4ZFwdX/2wk1HGtQrMr9Ch1ZbuwuIdOYKubYy5Sng5mq3Myr149DGgmnHMr684ozCA21O+wNOUf3vzzMWfXVT+k+hPPPsdN5tl3r80+kTvpn/3l/uNFPcQm89yZpidxGRzARybWXKc1h9lo1aUkXLSupt3Jo+dv/qz+a03r1pYKVDG4rq1zesXvzNDaut8eHnO83nF31HAzSsX7g+b8nYM2Nj3Oa3v9DbTLJGROvXcGX+0dZe1D4QEmoiSFzi0V49h3/Sq+fwT/C4HXtztp71Q8aXVyZWHNoQRMb/vf8wdeqi/16fMYKhB73/jrVw6VtX1TdhbJtYuwuIdOYKuZaSu77/1OHCesNjnW6NiSl3x8a4PbExZRoXW+6Jiy3TuNgy4uLKND62TOPjSiU+rlTj48qIjyuVhPhjJMSVSXz8MUdifKnEx5VKQlypIyH+mCM+rtSREF/qtP5e5oyPK41JiCt1xsWVOeNjS51xsWWx8XFlMXGxZTFxsWWxcXFlsbEx5bExTo/T7u9JMKhSdCSv7br1Cy76cWhDUc8XN69c8u2l65vRYHLyfb94cdFDNz9rZlXYZzfJ2t7uIiKZCeRaEkl/E7jE7jp+oh6gDCgT0XLx/ulweMpFtNzp8JQ7HB630+lxOx1ut9Pp8cTElJfHON2e2Bi3xsaUu2NjyjU2tkzjY8s8sbHlGh9bRnxcqcbFHv9BUnr8h4skxpcef82REH9M4uNKHT/+UIkvdSTElcYkxJc64mNLnfFxZTHx8aW1/oFyfGhjW/qY3FWfz2n+j3Mlb+cjzthtX1w4LPjfX+M09pOs5iZ3LZhf7aKOOIB4IF5VUAAFd8RcOPv2A8UR4/Y0GLXH0/S8rK1nlRQXrs880qW985YVdldfl8WqpzDL7iIinAnk2jP9EQKqih8orUv3M2n/XqbkFDPkYIPEWGkx5u+99g3J2am33NW0T3lCUq3W1zMCYr/dBUQ6E8i1ZwI50BqUHWFs3lam5hxmdH48nYo7EaNtgJaNcurlXHzfmE3nHNviThzwdMyMQRMblNc7YsI4PJz2MXajeiaQa88MwtdGrKeMwQe3cn5OLhNyhTML25Dg6YLQr+JmrTc3yZp576jdHTY37Hfhbb9k09Gi2AuzbtDyIZvH2lW6cYpyuwuIdCaQa++o3QVElF5HdjB5324m7Sun3+GmNCrvjpAMVDrnuNvyNutn3D+ysOnu+gNbdVnnvuy14bs/WTik4YxXXHG6atGAEFdvnF7YrLweqUwg116+3QWErVYluUzav50pOUUMPVifVse64qATPnQF6/dZ15UXPDo0pt6hhH6IxzPh+rsWnnXOm0Pe+HTi95c/6OpM7peHEUyfivBywO4CIl3UBbKItAKeBIZgNY4vxVrg9CDwEVDxRvBtqvqNiLiB77G+H1nAFap6yMdT5gWq9ohWr/woY/O2MDXnEKPz4+lc1IFYbQe0qHZfL3GLe8S/ei879+nUZnHFsQMAGrfevuvyx6bmJzY4NPql9y5Yfu399/dl/bcrSfQMD9rXYtRUnVmoIViiKpBFRIAPgddU9TLva52AqVj/WBaq6pRKdi1W1X7e7V8DfgU84uNp694VcoynnAGHtnH+vn1M2C/0OtKKRE9XhL41OtwxZ8mEp1NXjPxn707OcuePc4mHz/rToqEz/tJXhPZP/3vmkhsfvmMAT2xYQq/CU9twGuHAXCHXUlQFMjAWKFXV546/oKo7gL+JSJqPx1gCnOXHOaM/kM8ozGbyvl1M2ldK6uGmNCnrhnAGtVxtO6Eg7vAFjw3J6P9p914OlR+bBSU13p97+WMXZDVqsXsEwBMvXrn4zj/dNITR+Zu4batpNh++TCDXUrQFcm8g/TTvjxSRjAqfT1fVrcc/EREnMA54yY9z5vpXYphrcSyfc/dnMXUBCA9jAAAZmElEQVTfUYYdqEebki446AB0CNQpGuXUy5nxwIhNZ3zXLlWQE7qy9R3/r6Xjr7u3uzh0EMCDz1y3yPX09cNoVnaYr5c0RIgLVB1GwJlArqVoC+QTiMgzwAisceTbqXrIItEb1J2BVcDXfpxmV23rtE2iu5jReVuYmnOAsXlxdClqT5x2AJoF43StfmicNeve0bvbbWg26OQgjkssKLjs4RlrW3TK/LEXxZ1/vGnBEy9dORIB1szbSqyaWRXhLbouTmwQbYG8Hph+/BNV/ZWINAdWVrNfsar2E5FGwKdYY8h/9fGcuwEP4KhBvaHjUA+ph7Yydd8+zt2v9D7SiiR3V4SgP1TRdUXrDRffP7Kg6a4GgwTpcvL7PQZ/vnrqrTe0dDjdP4bxr35/5/y/vznTCu1/rZpPuxLT3zj8mSenaynaAvl/wKMi8ktVfdb7WpKvO6vqYRG5CfhIRJ5V1bLq90ktF0nPIdxWS+h6dDeT9mUzZd8xBhxqTNOybgg9gB6hKqHv513SL3h0qNQ/mFjpmnfO2JKSGfdetbxD7yUjRX564nHO3a55r314fhoAM/akc+nukZXtb4SdLXYXEOmiKpBVVUXkQuBJEbkD61eoo8Cd3k1OHkN+WFXfO+kYq0VkDVYHt3/6eOps7AzkpqWHmLB/K+fvK2REfhLtSjrjpB3QLtSliFvcw9/otfzcv57dOL44NrWq7dolL98484HL4mLiSk+YMXHxb/4w770vz0kDoFPRXt5e2REJ898+DLCGBXfaXUSkM+03A0Ak/V1gRkhOluAuYUT+FqbuO8C43Bi6HW1HvNq+dlzMMWfJ+GdSV4x8vXfHmHJnlfWIo7x88s03L0oe9skIkRMvCKb84ql5/zd/ZBpgPVK9/4tMGpebPhWRYbPiMquF1FJUXSHbaFNQjupQD30PZ3H+vr1M3K+kFLSgvrsbQtisW5ZQEHd46uNDMlI/OXHqWmWad8zMuuzhi4rikwrTTn5v7Jzn5n+7bOBPr3+55Dsal5tx48hhhisCwARyYKwOyFE6F+3lvH07mLLvGAMPNaJ5aTeEbkC3gBw/gBruS9o344GRmWcubtf/5BkTp1Ide/WDC1InvTxIhMSK73gUHXHZywuXZPT96Rg3blvCmHwTxpHFBHIAmEAODP8DuVHZYcbnbuP8nAJG5SfQobgzTtoAbQJfXuC03NJ4+6x7R2W3X9/8lKlrlWnYfNfeyx+furde47xTtnV7xDNwxr8Wr96Y/NM4ckpBFn9ZFza/ARg+22h3AdHABHJgZAGHgUaVvhvnKWXYgS1MzcnjnFwnPY62Jd7TGaHS2QfhqMvK1hsuvn/k4WbZDQYL0tmXfQZd+PfFo2b/IUVET7m5V+52uPte8NbSDVu7/TTMkVRexLIF5QgNAlW3ETLL7S4gGphADgDVVBVJXwOMQlRJKdjOlH17OG+/m74FzWlQ3h2hl9111kSfLzunX/jIMGlwoPKpa5VJbJB/YPajF25q0mZHpQ2ASstiynpNeW/l1p0dTnx/2cLVJJmmQRGoBFhrdxHRwARyoLyQ8SVTcxrQsrQrQhfglAcgIoV48Ax7o9eyiX8d0Di+qOqpa5XpPfq9FRN/dVsnh8MztLL3S47FHjvjvA/XZO9tfeL7j29YSMoRM984Mq1WXKY5fQCYQA6U63ZuhsgZgqiMs9RxbMIzqctHvpbSMabcWWmgViU2obDwEtclq1t3X1tlqBaVxBd3m/DR+pzcFoNOeGNE/kbu2DKoit2M8GeGKwLEBHLgLLa7gJqKPxJbMPUPQ9LP/rhHL4fn9FPXKtOl/7drp935s8bOmPIq9y0sSjzadfzHm3MPND2xH0Wz0oP877sGCPE1KN0IDyaQA8Q8GBJAgmsbETRU0XB/0v7pvxuxIXlh+/6CVH5D8jQcMaWl0+649rsu/eeNktM8TVdQmHSk87hPsw4WNDqxramosvPrlbQvGViD8o3w0V1xba1+M6M65go5sBYTAYHccmujHTPvG7Wzw/ctBgk+94k+QevuGZsveXCmxMaXnHb/g4cbHO58zqe7Cgrrn9pj+vX0+bQ//f5G2NtpwjhwTCAH1iLgcruLqErn9FYbL75v5KHmOxsOFqRmj1uLx3PeDbct7J323lCppjdx7oHGB7qc88m+o8VJvU95c/qedGbvNit/RL7P7S4gmphADqyv7C6gMilfdU6f9sgwGuQn+jVj4mRN227ZMfvRCw8n1C+o9oGQnNxmuV3Hf3yo+FhCz1PebF+cw9srO5imQVHBBHIAmTHkABNca/BvCajg1OHBM/StnsvOe2pgo/ii2FrPgR45+/GFgy/8e3/xYaXn7L0tc3pM/KjoWGlc11PejPGUs//LDTQps/17ZNRaKdBMcRXaXUi0MFfIgfchNgays9Rx7Jxn+y8f/WqfDjFl/k1dq0z9Jjn7L3986o4GzXJ8mn2xLbvt7uRJ75eXlceeGsYAXyxdTJMy06ciOiwyYRxYJpAD70PggVCfNP5IbMH5TwxePeCjHskOjyMgD1iknvfKkrFXu84Uh/o0C2JTVsedKee/6yh3x1Q+Pv2rrCWMO7WnhRGxPrO7gGhTZ4YsRMQNfF/hpQux1tD7CNgGJAKfquptJ+33EdBSVX2+2hRc24GQ9ChukJuYO901YkPP+R361WTqWmUS6h88dNnD0zc0a79lmK/7rPuha1a/C99KcHuclTdH6l2Qxffzmps+FVElWXEFp/VsHVWXrpCLVbVfxRdEpDPehU9FJBFYLSIfqOpi7/uNgVSgUES6qKqva4b9B7g1cKWfqkVWox0z7x21s+PaFgN96brmqzOHfrJqym9ubOtwenwO49Ubztw64OJ/NvB4nC0r3SCpvIjlpmlQlFllwjjw6lIgn5aqFnuXd6q47NF04BNgH9aSTo/5eLg3CFIgd1rdMnPmfaMONt/RcFCNp65VIia+qOji+y5f2S555Qnr21Vn6ZqUTcMufaWZqqN5lRstWZhOkmdEle8bkcjX5c0MP9SlQE6ssJ5elqpOq/imiDTBWgB0QYWXLwUexArk9/AxkBXXKrGuHgK2pE3KN51WT3tomDbIT6rV1LXKdExZvH76vVcmxcSW+TUveP6K/hvGXPVCG1VHkyo3enTDQs4yTYOiTDnwpt1FRKO6FMinDFl4jRSRtVjh+biq5gCISCugO7DIu3hquYikqOo6H8/3b+D3tSlYPHiGvN1z+XlPDWiYcDQu4I2LHM6ysvNvveG7HoO+HCGC0599v148+PsJ1z7TkdONW4/I38hdpmlQFPpKce23u4hoVJcCuSrHx5DPABZ5x5AzgFlAEyBLRAAaYg1b3OfjcWscyM5SR+m45/stT3v5rHYxZc4hNTlGdVp2Xrf10odnlMYlFPk9/vzx/0ZlXPCrP3cHqXpOctPSQ6ZpUNT6l90FRCsTyF6qullEHgPuxBqquBSYqKpLAESkC/A1Pgay4tomuBYDPjdcjy+MPTJl7uD0gR/0ONPhcQRpzFV1/HX3Lug74V+DRUjwd+93Ph+/atatj/UCSaxyI1ElY95mYtVcHUefI1hTO40gMIF8oueA27zh2xFYevwNVc0SkQIRGayqy3w83l/xIZDr5yXmznCN2NBzXod+gZwxcbJGrXbsuvyxqXlJDQ/W6ByvfThlxZy7XWeBnP6q95XV8+lgmgZFqVcUV7HdRUSrOjMP2Q6CywlspYo5yc23N9w5697ROzquaTFQEL+vVv0x9OKnFg2f+eezRGhYk/2fe2v60l8+eHcqyGkbCjFt72r+s+Is/ByTNiKCB+ihVptZIwhMIAeZ4Pot8MeKr3XMaLFp5n2jDrTY3miQIEENrqRGuXmXP3bB1kYtdw2u6TGefO2y7259/NZBIKf/jap9cQ7bv3bipEVNz2WEtQ8U10V2FxHNzJBF8L0I/A5o0Pu/nTKmPTSsvGFe0oDqdgqEs8a9uWzC9Xd1FYfWOIwfee6aRff95YahVPeDI8ZTzpp5+3Ha31jJCJo/211AtDNXyCEw8MN/3zf18SGXJBTGndoXOAjiEgsKLv39zLUtu2yo1Y3Be5+6YeGjz18zHKT6NplfLpnPhFzTpyJ6rVBc5iZtkJlADoG5KdntscaSTz/+GgDdB36VccFt1zd3ON3ta3OcWx67df5Tr182CqT6p/Z+kbWUZ78PyvQ8I2xcprjMwyBBZgI5ROamZL8AXBes4ztjS0qm3331so59Fo/y59Hnylz3wL3zX3z3It+udnsd2c66b5tSw5uFRkTYDPRWXOV2FxLtzBhy6DwOXAOBn33QLnnFxpkPXBYbE3es1kMGl9328Lw3/++8NJ82TnQXs2JBqQnjqHe/CePQMEvohMjt6zpsA14P5DHF4XZPvvnGeZc+NL17TNyx7rU93oW//uN8n8MY4LuFq0hyn1Hb8xphLR141+4i6gpzhRxa92M9fl31U24+atZhU9bsR6YVxScVptW6KuDca5+e/9Xiob5fYT+0cSH9CkzToOh3j+Iy45ohYsaQQ2xuSvbDwL01P4Jq2lUPLRww5cUBIiTVth6PoqOveHHBolX9fQ/joQcyWbyoMzV49NqIKPMU1xi7i6hLzBVy6D0OXAu08nfHBs137738sal76jfJ9atNZlXcHtEhs15btHJdb9/DuHHpYeYvrmfCuE642+4C6hozhhxit6/rUAjc4+9+A6c+t/j6Z4cl1W+Se3Yg6ih3O9z9p725eOW63r4PO4gqa+ZvIlY7BKIGI6y9pbiWVr+ZEUjmCrkKIlIIDOWnlRE6Aoe9H3mqeo53u1uwGte3UtXDPh7+FeB6oNqJ9gn1Dxyc/ei0zKZts3zuGledsnJneZ+p7yzflNXZvwdHXsqYT8fitEDVYYStI8Bv7S6iLjJjyFUQkUJVrV/h81exFkF976TtlgPHgJdU9VVfjz83JXsgsAyqnjPca+T7K8+78dYODofH7+GNqhwrjSntOen99Kzd7fx7kGPq3gw+XNHHNA2qE25VXE/aXURdZIYsakFEugH1sXokX+rPvrev67ACeKGy92Ljjx6d/ejUBZNv/s2AQIZx8bG4km7jP1njdxi3K97H+yvamTCuE9Kx2sYaNjCBXDuXYq0tthA4U0QqX3W5arcDOyu+0Lnv/O9vfK1PXtszMgJy4+64o8UJRV3Gfbp+9/6WA/3a0WoalGM6uNUJbuA6xeW2u5C6ygRy7VwCvKWqHuB94GJ/dr59XYcjwM8AHDGlpRfddfX8Gfdd0csZUx6w1aQBjhxNKuw09v8278tv5v8NwU+XLaZZWd9A1mOErScVV7rdRdRlJpBrSETOwlql+msR2Y4Vzn4NWwDcvq7DN+2Sl//hptf6ZHUb8N/R/i42Wp1DBfUPdxjzWVb+ocaVLfB6etdtX8a5poNbHbEW39eLNILE3NSrQnU39bzr7xWo6mMVtskC0lR1h18ny5R6wGqsgA+Y/EMND3Ue9+mewqJ6vfzeOfnIDjZ82xih6lWljWhRBAxQXBvtLqSuM1fINXcJ8MFJr33gfd0/yXoUuAwoq31Zlv35TfI7jvlsX43CONFdzMoFJSaM64xbTBiHB3OFHE4y5R7gkdoeZve+Fvu7n/tRQcmx+Jo1HEqft4j+BUFa9doIM/9RXDPsLsKwmCvk8PI48GVtDrB9d+u9Xcd/fLTGYfxgpgnjuiObIPboNvxnrpDDTaY0AVYCXf3ddcuO9rt6TvmPlpfH1OzR5iEHNvHdok6mT0WdUAqMVVyL7S7E+Im5Qg43yXoQmIZ1o8VnG7d23tFz8n+kxmHcqOww8xcnmDCuM35hwjj8mEAOR8m6FpgD+PTry5pNPbb1mfpOfLk7pl2NzieqZMzLJE4DOv/ZCFt/VFyv2F2EcSoTyOEqWd8FflfdZiu+7/VD6rQ3Grg9ztY1Ptc/1iygc/HgGu9vRJJPgDvtLsKonAlkH3m7v1X8fI6IPO39+6siMqOy7UWks4ioiNxY4b2nRWROtSdN1oeAN6p6+7vVZ2UOnvVaM486av5Y8+ScNVyz09zEqxu+x1o92mN3IUblTCCHxn7gZhGJq8G+1wALTn7x22UD1o+47OU2qo6mNa6qbfF+PlrexjQNqhP2AucrrsJqtzRsYwI5NHKB/wJX+b1nsh4DzgdWHX/pswXD1o6d81xHRWr+4IZT3ayZtwcn/jZEMiJPHnCO4vLvCVIj5EyDet8likhGhc+bAh/7sf/jwOci8rLfZ07WAjLlXGD++1+PKZ1+09wzQOr5fZyKPlm2kOZlabU6hhEJDgHjFdcGuwsxqmeukH1XrKr9jn8AD1R4r7LZECe8pqpZwHKsR6T9l6z5uQcan3PxzU/E1DqMr92xjIn7TdOg6HcEmKi4Mqrd0ggLJpADIx9ocvwTEWmK9WviyR7FusNdo+97i2EHczzqOA/YUpP9ATijcCfPr0lGql6pxIgKRcAUxbXM7kIM35lADox5wKwKN+3mAN+evJGqZgIbgCk1PZFq6m5gDLDV750T3CWkzy/GYZoGRbmjwAWK65SbwUZ4M4EcAKr6KdaqIau848zDqXqu5yNA+9qdL3UXMBKrh63vFi1aQT33mbU5txH28rAeif7G7kIM/5leFhFMJL0R1kT/kdVu/MCmRTy4ycw3jm47gAmKa7PdhRg1YwI5womkJwD/Bi6qcqPBBzexZGFHhMSQFWaE2vdYN/D22F2IUXNmyCLCqaaWYK3lV/my7Y3KDrNgUYIJ46i2ABhlwjjymUCOAqqpHtXUW4EbgfIT3kyfv9E0DYpqL2INUxyyuxCj9kwgB5mITPP2skj2fp4mIp+etM2PvTBEZIqIrBaRNSKyQUSu9/VcqqlPA+OxHtWGFzLm07VoSMC+GCOcHAN+rriuU1zH7C7GCAwTyMF3KbAIH9baE5FY4AXgfFXtC/THmlLnM9XUecDZXLD3Ha7dOdzvao1IkA2MVFz/sLsQI7BMIAeRiNTHmgL3M3xb/LQB1uPs+QCqekxVN/l7XtXUXXy44gqEZ/zd1wh7/wPOVlwr7C7ECDwTyMF1IfCFqm4GDohI6uk2VtUDWP0xdojImyIyW0Rq9N9IcZUqrt9grT5ysCbHMMJKOfAg1nhxrt3FGMFhAjm4LgXe8v79Le/nVc0zVABVvRYYh9X34jbA/2ZEJxzU9SGQAnxa3bZG2FoPDFZcLsXltrsYI3jMPOQgEZFmwC6sG2wKOL1/TgaeU9XhFbb9GPiTqs4/6RjNgSxVbRCQmnBdCTxFhb4bRljzAH8EHjA37uoGc4UcPDOA11W1k6p2VtUOQBZW2862ItITQEQ6AX2BDBGpLyJpFY7RD+vpq4BQXK8DvfGvbahhjy1YN+7uNGFcd5gr5CARkXnA46r6RYXXbgJ6Av8C/gQkAGXAPar6tYg0AN4GugHFWE1iblbVlQGvD9ds4M9gGtSHmWLgCeAPiqvY7mKM0DKBXIcJrobAPcBvgHibyzHgXeA2xbXT7kIMe5hANhBcXbCuymZUt60RFGuBmxXXPLsLMexlAtn4keAaiTWMMcDuWuqIPcDDwAtm9oQBJpCNkwguAaYC9wIDbS4nWuVgrbH4vOIqsbsYI3yYQDaqJLjGY40xp9lcSrTYiTWN7UVzw86ojAlko1qCaxhWME8CsxZfDawG/gb8S3GV2V2MEb5MIBs+E1w9gGux1gw00+VOrxhrCuNzZqFRw1cmkA2/Ca5Y4ALgOqx2n+aq+SebgOeBVxWX6SFi+MUEslEr3ilzV2ItIXWWzeXYZSfwH+BdxbXE7mKMyGUC2QgYwdUNq7vcNGAo0X3lnMVPIbw8EAcUkfbAM0AvrLYGnwKfAX/wbtId2I01HLIWq/HUbao6pcIxXgU+VdX3vE+LtvFuD7BFVWeIiAvrt5tcIA54SFXfDMTXYNROjN0FGNFDcW3FmkXwR8HVBmtYYxwwisgfcy4EFgLfAt8ortWBPLiICPA+8KyqXiAiTqzFCs5R1X7ebeZhBfBK7+dpPhx6dhWP3j+pqn8UkR7AKhF5T1XNDUebmUA2gkJx7QWe834guJKxgvn4Rwf7qvPJEWApVgB/C6xUXOWn36VWxgIlqvoKgKq6ReQWIEtEfqeqRcE4qar+ICJFWB0A9wfjHIbvTCAbIaG4MoFMrKs+BFd7oA9Wr+be3j97AkkhLw22AWuwhgGO/5mluEI5ntcbWHVCYaoFIrITa6hibRX7jRSRjAqfd+TE3tf/FpHjQxZfq+rtFXf2Lprwg6qaMA4DJpANWyiuXVj9oj8//prgcgBdgB5YV9DtgXZAK+9HS6AhViOkeKwe05Xx8FO3vCKsq9093vMd/8j2/rldcR0N7FdXI0LlixdU9fpxCysZQ66oqiGLW0TkOqArMNHPWo0gMYFshA3F5QG2ej+qJbhi+Cmc44FS4GiEPo68Hphe8QURaYj1g8mn74efjo8hXwS8LiLdVDUSv29RxQSyEbG8Y7rlWFfCke6/wOMicqWqvu69qfcn4NVgjR8DqOr7InIVcBXW/GnDRmbFEMMIA2rNP50GXCwiPwCbgRKsR9Zr498ikuH9+KaKbX4P3FrTBXWNwDHzkA3DMMKE+YloGIYRJkwgG4ZhhAkTyIZhGGHCBLJhGEaYMIFsGIYRJkwgG4ZhhAkTyIZhGGHCBLJhGEaYMIFsGIYRJkwgG4ZhhAkTyIZhGGHCBLJhGEaYMIFsGIYRJkwgG4ZhhAkTyIZhGGHCBLJhGEaYMIFsGIYRJkwgG4ZhhIn/ByBEIfAjk6D2AAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -748,17 +1156,19 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -767,10 +1177,8 @@ "sea = np.loadtxt('holland_seawater.dat')\n", "plt.fill_between(range(1, 13), air, color='b', alpha=0.3)\n", "plt.fill_between(range(1, 13), sea, color='r', alpha=0.3)\n", - "plt.xticks(np.linspace(0, 11, 12), ['jan', 'feb', 'mar', 'apr',\\\n", + "plt.xticks(np.arange(1, 13), ['jan', 'feb', 'mar', 'apr',\\\n", " 'may', 'jun', 'jul', 'aug', 'sep', ' oct', 'nov', 'dec'])\n", - "plt.xlim(1, 12)\n", - "plt.ylim(0, 20)\n", "plt.xlabel('Month')\n", "plt.ylabel('Temperature (Celcius)');" ] @@ -800,7 +1208,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.8.2" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false }, "varInspector": { "cols": { @@ -837,5 +1263,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/notebook2_arrays/py_exploratory_comp_2.ipynb b/notebook2_arrays/py_exploratory_comp_2.ipynb index f7ffd3f..daaa4a7 100644 --- a/notebook2_arrays/py_exploratory_comp_2.ipynb +++ b/notebook2_arrays/py_exploratory_comp_2.ipynb @@ -18,7 +18,7 @@ "source": [ "## Notebook 2: Arrays\n", "\n", - "In this notebook, we will do math on arrays using functions of the `numpy` package. A nice overview of `numpy` functionality can be found [here](http://wiki.scipy.org/Tentative_NumPy_Tutorial). We will also make plots. We start by telling the Jupyter Notebooks to put all graphs inline. Then we import the `numpy` package and call it `np`, and we import the plotting part of the `matplotlib` package and call it `plt`. We will add these three lines at the top of all upcoming notebooks as we will always be using `numpy` and `matplotlib`. " + "In this notebook, we will do math on arrays using functions of the `numpy` package. A nice overview of `numpy` functionality can be found [here](https://docs.scipy.org/doc/numpy/user/quickstart.html). We will also make plots. We start by telling the Jupyter Notebooks to put all graphs inline. Then we import the `numpy` package and call it `np`, and we import the plotting part of the `matplotlib` package and call it `plt`. We will add these three lines at the top of all upcoming notebooks as we will always be using `numpy` and `matplotlib`. " ] }, { @@ -128,16 +128,16 @@ "x = np.arange(20, 30)\n", "print(x)\n", "print(x[0:5])\n", - "print(x[:5]) # same as previous one\n", + "print(x[:5]) # same as previous one\n", "print(x[3:7])\n", - "print(x[2:9:2]) # step is 2" + "print(x[2:9:2]) # step is 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can also start at the end and count back. Generally, the index of the end is not known. You can find out how long the array is and access the last value by typing `x[len(x) - 1]` but it would be inconvenient to have to type `len(arrayname)` all the time. Luckily, there is a shortcut: `x[-1]` is the same as `x[len(x) - 1]` represents the last value in the array. For example:" + "You can also start at the end and count back. Generally, the index of the end is not known. You can find out how long the array is and access the last value by typing `x[len(x) - 1]` but it would be inconvenient to have to type `len(arrayname)` all the time. Luckily, there is a shortcut: `x[-1]` is the same as `x[len(x) - 1]` and represents the last value in the array. For example:" ] }, { @@ -158,7 +158,7 @@ "metadata": {}, "source": [ "You can assign one value to a range of an array by specifying a range of indices, \n", - "or you can assign an array to a range of another array, as long as the ranges have equal length. In the last example below, the first 5 values of `x` (specified as `x[0:5]`) are given the values `[40, 42, 44, 46, 48]`." + "or you can assign an array to a range of another array, as long as the ranges have the same length. In the last example below, the first 5 values of `x` (specified as `x[0:5]`) are given the values `[40, 42, 44, 46, 48]`." ] }, { @@ -180,7 +180,7 @@ "metadata": {}, "source": [ "### Exercise 1, Arrays and indices\n", - "Create an array of zeros with length 20. Change the first 5 values to 10. Change the next 10 values to a sequence starting at 12 and increasig with steps of 2 to 30 - do this with one command. Set the final 5 values to 30. Plot the value of the array on the $y$-axis vs. the index of the array on the $x$-axis. Draw vertical dashed lines at $x=4$ and $x=14$ (i.e., the section between the dashed lines is where the line increases from 10 to 30). Set the minimum and maximum values of the $y$-axis to 8 and 32 using the `ylim` command." + "Create an array of zeros with length 20. Change the first 5 values to 10. Change the next 10 values to a sequence starting at 12 and increasig with steps of 2 to 30 (do this with one command). Set the final 5 values to 30. Plot the value of the array on the $y$-axis vs. the index of the array on the $x$-axis. Draw vertical dashed lines at $x=4$ and $x=14$ (i.e., the section between the dashed lines is where the line increases from 10 to 30). Set the minimum and maximum values of the $y$-axis to 8 and 32 using the `ylim` command." ] }, { @@ -218,14 +218,14 @@ "alist[0] = 7 # Since alist is a list, you can change values \n", "print('modified alist', alist)\n", "#btuple[0] = 100 # Will give an error\n", - "#print 2*alist" + "#print(2 * alist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Lists and tuples are versatile data types in Python. We already used lists without realizing it when we created our first array with the command `np.array([1, 7, 2, 12])`. What we did is we gave the `array` function one input argument: the list `[1, 7, 2, 12]`, and the `array` function returned a one-dimensional array with those values. Lists and tuples can consist of a sequences of pretty much anything, not just numbers. In the example given below, `alist` contains 5 *things*: the integer 1, the float 20, the word `python`, an array with the values 1,2,3, and finally, the function `len`. The latter means that `alist[4]` is actually the function `len`. That function can be called to determine the length of an array as shown below. The latter may be a bit confusing, but it is cool behavior if you take the time to think about it." + "Lists and tuples are versatile data types in Python. We already used lists without realizing it when we created our first array with the command `np.array([1, 7, 2, 12])`. What we did is we gave the `array` function one input argument: the list `[1, 7, 2, 12]`, and the `array` function returned a one-dimensional array with those values. Lists and tuples can consist of a sequences of pretty much anything, not just numbers. In the example given below, `alist` contains 5 *things*: the integer 1, the float 20.0, the word `python`, an array with the values 1,2,3, and finally, the function `len`. The latter means that `alist[4]` is actually the function `len`. That function can be called to determine the length of an array as shown below. The latter may be a bit confusing, but it is cool behavior if you take the time to think about it." ] }, { @@ -333,8 +333,8 @@ "* the first column of `x`\n", "* the third row of `x`\n", "* the last two columns of `x`\n", - "* the four values in the upper right-hand corner of `x`\n", - "* the four values at the center of `x`\n", + "* the 2 by 2 block of values in the upper right-hand corner of `x`\n", + "* the 2 by 2 block of values at the center of `x`\n", "\n", "`x = np.array([[4, 2, 3, 2],\n", " [2, 4, 3, 1],\n", @@ -361,7 +361,7 @@ "metadata": {}, "source": [ "### Visualizing two-dimensional arrays\n", - "Two-dimensonal arrays can be visualized with the `plt.matshow` function. In the example below, the array is very small (only 4 by 4), but it illustrates the general principle. A colorbar is added as a legend. The ticks in the colorbar are specified to be 2, 4, 6, and 8. Note that the first row of the matrix (with index 0), is plotted at the top, which corresponds to the location of the first row in the matrix." + "Two-dimensonal arrays can be visualized with the `plt.matshow` function. In the example below, the array is very small (only 4 by 4), but it illustrates the general principle. A colorbar is added as a legend. The ticks in the colorbar are specified to be 2, 4, 6, and 8. Note that the first row of the array (with index 0), is plotted at the top, which corresponds to the location of the first row in the array." ] }, { @@ -402,7 +402,7 @@ "metadata": {}, "source": [ "### Exercise 3, Create and visualize an array\n", - "Create an array of size 10 by 10. The upper left-hand quadrant of the array should get the value 4, the upper right-hand quadrant the value 3, the lower right-hand quadrant the value 2 and the lower left-hand quadrant the value 1. First create an array of 10 by 10 using the `zeros` command, then fill each quadrant by specifying the correct index ranges. Note that the first index is the row number. The second index runs from left to right. Visualize the array using `matshow`. It should give a red, yellow, light blue and dark blue box (clock-wise starting from upper left) when you use the default `jet` colormap." + "Create an array of size 10 by 10. Set the upper left-hand quadrant of the array should to 4, the upper right-hand quadrant to 3, the lower right-hand quadrant t0 2 and the lower left-hand quadrant to 1. First create an array of 10 by 10 using the `zeros` command, then fill each quadrant by specifying the correct index ranges. Visualize the array using `matshow`. It should give a red, yellow, light blue and dark blue box (clock-wise starting from upper left) when you use the `jet` colormap." ] }, { @@ -424,9 +424,9 @@ "metadata": {}, "source": [ "### Exercise 4, Create and visualize a slightly fancier array\n", - "Consider the image shown below, which roughly shows the letters TU. You are asked to create an array that represents the same TU. First create a zeros array of 11 rows and 17 columns. Give the background value 0, the letter T value -1, and the letter U value +1. \n", + "Consider the image shown below, which roughly shows the letters TU. You are asked to create an array that represents the same TU. First create a zeros array of 11 rows and 17 columns. Give the background value 0, the letter T value -1, and the letter U value +1. Use the `jet` colormap. \n", "\n", - "" + "![](tufig.png)" ] }, { @@ -477,19 +477,20 @@ "outputs": [], "source": [ "a = 4\n", - "print(a < 4)\n", - "print(a <= 4) # a is smaller than or equal to 4\n", - "print(a == 4) # a is equal to 4. Note that there are 2 equal signs\n", - "print(a >= 4) \n", - "print(a > 4)\n", - "print(a != 4) # a is not equal to 4" + "print('the value of a is', a)\n", + "print('a < 4: ', a < 4)\n", + "print('a <= 4:', a <= 4) # a is smaller than or equal to 4\n", + "print('a == 4:', a == 4) # a is equal to 4. Note that there are 2 equal signs\n", + "print('a >= 4:', a >= 4) \n", + "print('a > 4: ', a > 4)\n", + "print('a != 4:', a != 4) # a is not equal to 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "It is important to understand the difference between one equal sign like `a=4` and two equal signs like `a==4`. One equal sign means assignment. Whatever is on the right side of the equal sign is assigned to what is on the left side of the equal sign. Two equal signs is a comparison and results in either `True` (when both sides are equal) or `False`." + "It is important to understand the difference between one equal sign like `a = 4` and two equal signs like `a == 4`. One equal sign means assignment. Whatever is on the right side of the equal sign is assigned to what is on the left side of the equal sign. Two equal signs is a comparison and results in either `True` (when both sides are equal) or `False`." ] }, { @@ -526,7 +527,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The statement `data<3` returns an array of type `boolean` that has the same length as the array `data` and for each item in the array it is either `True` or `False`. The cool thing is that this array of `True` and `False` values can be used to specify the indices of an array:" + "The statement `data < 3` returns an array of type `boolean` that has the same length as the array `data` and for each item in the array it is either `True` or `False`. The cool thing is that this array of `True` and `False` values can be used to specify the indices of an array:" ] }, { @@ -581,7 +582,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Exercise 5, Replace high and low in an array\n", + "### Exercise 5, Replace high and low values in an array\n", "Create an array for variable $x$ consisting of 100 values from 0 to 20. Compute $y=\\sin(x)$ and plot $y$ vs. $x$ with a blue line. Next, replace all values of $y$ that are larger than 0.5 by 0.5, and all values that are smaller than $-$0.75 by $-$0.75, and plot the modified $y$ values vs. $x$ using a red line on the same graph. " ] }, @@ -839,7 +840,7 @@ "metadata": {}, "outputs": [], "source": [ - "x = np.linspace(0, 6 * np.pi, 50)\n", + "x = np.linspace(0, 20, 100)\n", "y = np.sin(x)\n", "plt.plot(x[y > 0], y[y > 0], 'bo')\n", "plt.plot(x[y <= 0], y[y <= 0], 'ro');" @@ -912,7 +913,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.8.2" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false }, "varInspector": { "cols": { @@ -949,5 +968,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/notebook2_arrays/py_exploratory_comp_2_sol.ipynb b/notebook2_arrays/py_exploratory_comp_2_sol.ipynb index 83e5874..aa8eea7 100644 --- a/notebook2_arrays/py_exploratory_comp_2_sol.ipynb +++ b/notebook2_arrays/py_exploratory_comp_2_sol.ipynb @@ -18,7 +18,7 @@ "source": [ "## Notebook 2: Arrays\n", "\n", - "In this notebook, we will do math on arrays using functions of the `numpy` package. A nice overview of `numpy` functionality can be found [here](http://wiki.scipy.org/Tentative_NumPy_Tutorial). We will also make plots. We start by telling the Jupyter Notebooks to put all graphs inline. Then we import the `numpy` package and call it `np`, and we import the plotting part of the `matplotlib` package and call it `plt`. We will add these three lines at the top of all upcoming notebooks as we will always be using `numpy` and `matplotlib`. " + "In this notebook, we will do math on arrays using functions of the `numpy` package. A nice overview of `numpy` functionality can be found [here](https://docs.scipy.org/doc/numpy/user/quickstart.html). We will also make plots. We start by telling the Jupyter Notebooks to put all graphs inline. Then we import the `numpy` package and call it `np`, and we import the plotting part of the `matplotlib` package and call it `plt`. We will add these three lines at the top of all upcoming notebooks as we will always be using `numpy` and `matplotlib`. " ] }, { @@ -36,7 +36,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### One-dimesional arrays\n", + "### One-dimensional arrays\n", "There are many ways to create arrays. For example, you can enter the individual elements of an array" ] }, @@ -179,16 +179,16 @@ "x = np.arange(20, 30)\n", "print(x)\n", "print(x[0:5])\n", - "print(x[:5]) # same as previous one\n", + "print(x[:5]) # same as previous one\n", "print(x[3:7])\n", - "print(x[2:9:2]) # step is 2" + "print(x[2:9:2]) # step is 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can also start at the end and count back. Generally, the index of the end is not known. You can find out how long the array is and access the last value by typing `x[len(x) - 1]` but it would be inconvenient to have to type `len(arrayname)` all the time. Luckily, there is a shortcut: `x[-1]` is the same as `x[len(x) - 1]` represents the last value in the array. For example:" + "You can also start at the end and count back. Generally, the index of the end is not known. You can find out how long the array is and access the last value by typing `x[len(x) - 1]` but it would be inconvenient to have to type `len(arrayname)` all the time. Luckily, there is a shortcut: `x[-1]` is the same as `x[len(x) - 1]` and represents the last value in the array. For example:" ] }, { @@ -220,7 +220,7 @@ "metadata": {}, "source": [ "You can assign one value to a range of an array by specifying a range of indices, \n", - "or you can assign an array to a range of another array, as long as the ranges have equal length. In the last example below, the first 5 values of `x` (specified as `x[0:5]`) are given the values `[40, 42, 44, 46, 48]`." + "or you can assign an array to a range of another array, as long as the ranges have the same length. In the last example below, the first 5 values of `x` (specified as `x[0:5]`) are given the values `[40, 42, 44, 46, 48]`." ] }, { @@ -252,7 +252,7 @@ "metadata": {}, "source": [ "### Exercise 1, Arrays and indices\n", - "Create an array of zeros with length 20. Change the first 5 values to 10. Change the next 10 values to a sequence starting at 12 and increasig with steps of 2 to 30 - do this with one command. Set the final 5 values to 30. Plot the value of the array on the $y$-axis vs. the index of the array on the $x$-axis. Draw vertical dashed lines at $x=4$ and $x=14$ (i.e., the section between the dashed lines is where the line increases from 10 to 30). Set the minimum and maximum values of the $y$-axis to 8 and 32 using the `ylim` command." + "Create an array of zeros with length 20. Change the first 5 values to 10. Change the next 10 values to a sequence starting at 12 and increasig with steps of 2 to 30 (do this with one command). Set the final 5 values to 30. Plot the value of the array on the $y$-axis vs. the index of the array on the $x$-axis. Draw vertical dashed lines at $x=4$ and $x=14$ (i.e., the section between the dashed lines is where the line increases from 10 to 30). Set the minimum and maximum values of the $y$-axis to 8 and 32 using the `ylim` command." ] }, { @@ -300,14 +300,14 @@ "alist[0] = 7 # Since alist is a list, you can change values \n", "print('modified alist', alist)\n", "#btuple[0] = 100 # Will give an error\n", - "#print 2*alist" + "#print(2 * alist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Lists and tuples are versatile data types in Python. We already used lists without realizing it when we created our first array with the command `np.array([1, 7, 2, 12])`. What we did is we gave the `array` function one input argument: the list `[1, 7, 2, 12]`, and the `array` function returned a one-dimensional array with those values. Lists and tuples can consist of a sequences of pretty much anything, not just numbers. In the example given below, `alist` contains 5 *things*: the integer 1, the float 20, the word `python`, an array with the values 1,2,3, and finally, the function `len`. The latter means that `alist[4]` is actually the function `len`. That function can be called to determine the length of an array as shown below. The latter may be a bit confusing, but it is cool behavior if you take the time to think about it." + "Lists and tuples are versatile data types in Python. We already used lists without realizing it when we created our first array with the command `np.array([1, 7, 2, 12])`. What we did is we gave the `array` function one input argument: the list `[1, 7, 2, 12]`, and the `array` function returned a one-dimensional array with those values. Lists and tuples can consist of a sequences of pretty much anything, not just numbers. In the example given below, `alist` contains 5 *things*: the integer 1, the float 20.0, the word `python`, an array with the values 1,2,3, and finally, the function `len`. The latter means that `alist[4]` is actually the function `len`. That function can be called to determine the length of an array as shown below. The latter may be a bit confusing, but it is cool behavior if you take the time to think about it." ] }, { @@ -470,8 +470,8 @@ "* the first column of `x`\n", "* the third row of `x`\n", "* the last two columns of `x`\n", - "* the four values in the upper right-hand corner of `x`\n", - "* the four values at the center of `x`\n", + "* the 2 by 2 block of values in the upper right-hand corner of `x`\n", + "* the 2 by 2 block of values at the center of `x`\n", "\n", "`x = np.array([[4, 2, 3, 2],\n", " [2, 4, 3, 1],\n", @@ -498,7 +498,7 @@ "metadata": {}, "source": [ "### Visualizing two-dimensional arrays\n", - "Two-dimensonal arrays can be visualized with the `plt.matshow` function. In the example below, the array is very small (only 4 by 4), but it illustrates the general principle. A colorbar is added as a legend. The ticks in the colorbar are specified to be 2, 4, 6, and 8. Note that the first row of the matrix (with index 0), is plotted at the top, which corresponds to the location of the first row in the matrix." + "Two-dimensonal arrays can be visualized with the `plt.matshow` function. In the example below, the array is very small (only 4 by 4), but it illustrates the general principle. A colorbar is added as a legend. The ticks in the colorbar are specified to be 2, 4, 6, and 8. Note that the first row of the array (with index 0), is plotted at the top, which corresponds to the location of the first row in the array." ] }, { @@ -518,12 +518,14 @@ }, { "data": { - "image/png": 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -552,12 +554,14 @@ "outputs": [ { "data": { - "image/png": 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -571,7 +575,7 @@ "metadata": {}, "source": [ "### Exercise 3, Create and visualize an array\n", - "Create an array of size 10 by 10. The upper left-hand quadrant of the array should get the value 4, the upper right-hand quadrant the value 3, the lower right-hand quadrant the value 2 and the lower left-hand quadrant the value 1. First create an array of 10 by 10 using the `zeros` command, then fill each quadrant by specifying the correct index ranges. Note that the first index is the row number. The second index runs from left to right. Visualize the array using `matshow`. It should give a red, yellow, light blue and dark blue box (clock-wise starting from upper left) when you use the default `jet` colormap." + "Create an array of size 10 by 10. Set the upper left-hand quadrant of the array should to 4, the upper right-hand quadrant to 3, the lower right-hand quadrant t0 2 and the lower left-hand quadrant to 1. First create an array of 10 by 10 using the `zeros` command, then fill each quadrant by specifying the correct index ranges. Visualize the array using `matshow`. It should give a red, yellow, light blue and dark blue box (clock-wise starting from upper left) when you use the `jet` colormap." ] }, { @@ -593,9 +597,9 @@ "metadata": {}, "source": [ "### Exercise 4, Create and visualize a slightly fancier array\n", - "Consider the image shown below, which roughly shows the letters TU. You are asked to create an array that represents the same TU. First create a zeros array of 11 rows and 17 columns. Give the background value 0, the letter T value -1, and the letter U value +1. \n", + "Consider the image shown below, which roughly shows the letters TU. You are asked to create an array that represents the same TU. First create a zeros array of 11 rows and 17 columns. Give the background value 0, the letter T value -1, and the letter U value +1. Use the `jet` colormap. \n", "\n", - "" + "![](tufig.png)" ] }, { @@ -657,30 +661,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "False\n", - "True\n", - "True\n", - "True\n", - "False\n", - "False\n" + "the value of a is 4\n", + "a < 4: False\n", + "a <= 4: True\n", + "a == 4: True\n", + "a >= 4: True\n", + "a > 4: False\n", + "a != 4: False\n" ] } ], "source": [ "a = 4\n", - "print(a < 4)\n", - "print(a <= 4) # a is smaller than or equal to 4\n", - "print(a == 4) # a is equal to 4. Note that there are 2 equal signs\n", - "print(a >= 4) \n", - "print(a > 4)\n", - "print(a != 4) # a is not equal to 4" + "print('the value of a is', a)\n", + "print('a < 4: ', a < 4)\n", + "print('a <= 4:', a <= 4) # a is smaller than or equal to 4\n", + "print('a == 4:', a == 4) # a is equal to 4. Note that there are 2 equal signs\n", + "print('a >= 4:', a >= 4) \n", + "print('a > 4: ', a > 4)\n", + "print('a != 4:', a != 4) # a is not equal to 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "It is important to understand the difference between one equal sign like `a=4` and two equal signs like `a==4`. One equal sign means assignment. Whatever is on the right side of the equal sign is assigned to what is on the left side of the equal sign. Two equal signs is a comparison and results in either `True` (when both sides are equal) or `False`." + "It is important to understand the difference between one equal sign like `a = 4` and two equal signs like `a == 4`. One equal sign means assignment. Whatever is on the right side of the equal sign is assigned to what is on the left side of the equal sign. Two equal signs is a comparison and results in either `True` (when both sides are equal) or `False`." ] }, { @@ -736,7 +742,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The statement `data<3` returns an array of type `boolean` that has the same length as the array `data` and for each item in the array it is either `True` or `False`. The cool thing is that this array of `True` and `False` values can be used to specify the indices of an array:" + "The statement `data < 3` returns an array of type `boolean` that has the same length as the array `data` and for each item in the array it is either `True` or `False`. The cool thing is that this array of `True` and `False` values can be used to specify the indices of an array:" ] }, { @@ -818,7 +824,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Exercise 5, Replace high and low in an array\n", + "### Exercise 5, Replace high and low values in an array\n", "Create an array for variable $x$ consisting of 100 values from 0 to 20. Compute $y=\\sin(x)$ and plot $y$ vs. $x$ with a blue line. Next, replace all values of $y$ that are larger than 0.5 by 0.5, and all values that are smaller than $-$0.75 by $-$0.75, and plot the modified $y$ values vs. $x$ using a red line on the same graph. " ] }, @@ -868,12 +874,14 @@ "outputs": [ { "data": { - "image/png": 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EYu02swGsW5d0H+WV28QatStpH7Atfb0NuLO3gqTlwFeAL0TEl3rWrUnnAq4GHh+xPWY2DGdRWY5RA8NNwOWSDgGXp8tI6kj6TFrnfcA7gQ/kpKXulfQY8BiwGvijEdszuZxZYkvRxCwqGztFNK9XptPpxMzMTNXNqI/ezBJIjvr8BTezDEkPR0RnsXq+8rkNnFliZgVyYGgDZ5aYWYEcGNrA9+cxswI5MLSBM0vMrEAODG3gzBKzdqhJdqGf4NYWTbo/j5nNV6P7VvmMwcysDmqUXejAYGZWBzXKLnRgMDOrgxplFzowmJnVQY2yCx0YzGwy1SQD6O/UKLvQWUlmNnlqlAHUpSbZhT5jMLPJU6MMoDpyYDCzyVOjDKA6cmAws8lTowygOnJgMLPJU6MMoDoaKTBIWiVpv6RD6Xxln3onM09v25cpP0/Sg+n2t6ePAbW2qFvWh9mcGmUA1dFIT3CT9MfASxFxk6QdwMqI+EhOvR9FxJk55XcAfx0Rt0n6c+CRiPjUYu/rJ7g1gJ8qZ1Y7ZT3BbTNwa/r6VuDqQTeUJOBdwJeXsr3VnLM+zBpr1MDw5og4DpDO39Sn3hskzUh6QNLcj//ZwA8i4rV0+SiwdsT2WF0468OssRa9wE3SPcBbclYNc+i3LiKOSTof+Lqkx4Af5tTr268laTuwHWCdMwfqb9265KKhvHIzq7VFzxgi4rKI+Pmc6U7gBUlrANL5i33+xrF0/gxwP3AR8D3gLElzwekc4NgC7dgdEZ2I6ExNTQ3xT7RKOOsjl8fjrQlG7UraB2xLX28D7uytIGmlpDPS16uBdwBPRDLqfR9wzULbW0M562OeufH4Z5+FiNfvwuDgYHUzalbS2cAdwDrgOeDXIuIlSR3g+oj4kKRfAj4NnCIJRJ+MiM+m258P3AasAv438JsR8epi7+usJGui9evze9emp+HIkbJbY5No0KykkQJDVRwYrIlOOy05U+glwalT5bfHJk9Z6apmNiDfhcGawoHBrCRVj8d74NsG5cBgVpIqx+M98G3D8BiD2QTwwLeBxxjMLMMXotswHBjMJoAHvm0YDgwt5wFHg+oHvq1ZHBhazAOONscXotswPPjcYh5wNLMsDz6bBxzNbEkcGFrMA45mthQODC3mAUezeqp7UogDQ4t5wNGsfpqQFOLBZzOzElWZFOLBZzOzGmpCUogDg5lZxrj7/5uQFDJSYJC0StJ+SYfS+cqcOr8s6UBm+n+Srk7XfV7SdzPrLhylPWZmoyij/78JSSGjnjHsAO6NiA3Avelyl4i4LyIujIgLgXcBJ4CvZar8wdz6iDgwYnvMzJZs5044caK77MSJpLwoTUgKOX3E7TcDl6avbwXuBz6yQP1rgK9GxIkF6piZVaKs/v+tW+sVCHqNesbw5og4DpDO37RI/S3AF3vKdkl6VNLNks7ot6Gk7ZJmJM3Mzs6O1morVN1zss0G1YT+/zIsGhgk3SPp8Zxp8zBvJGkN8I+AuzPFHwX+IfBPgVUscLYREbsjohMRnampqWHe2saoCTnZZoNqQv9/GRYNDBFxWUT8fM50J/BC+oM/98P/4gJ/6n3AVyLiJ5m/fTwSrwJ/CWwc7Z9jZSujT9asLE3o/y/DqF1J+4Bt6ettwJ0L1L2Wnm6kTFARcDXw+IjtsZI1ISfbbBhbtyYXmp06lcwnLSjA6IHhJuBySYeAy9NlJHUkfWaukqT1wLnA/+rZfq+kx4DHgNXAH43YHiuZ+2SL4XEaq5ORspIi4vvAu3PKZ4APZZaPAGtz6r1rlPe36u3alYwpZLuTJrFPdhRz4zRz+3BunAYm82jVqucrn20k7pMdncdprG58Ez2zip12WpLR1UtK+rnNiuKb6Jk1RJHjNB6rsCI4MJhVrKjceV9TYkVxYDCrWFHjNB6rsKI4MEwodznUSxG5876mxIriwDCB3OXQTr6mxIriwDCB3OXQTr7PjxXFgWECucuhnXxNSf00tct21OcxWAOtW5f/MHJ3OTRf3e/zP0mafEW7zxgmkLsczIY37NF/k7tsHRgmkLsczIazlISNJnfZOjBMqKWkRza1v9RsVEs5+m9ylpgDgw3EKa7VcDCuh6Uc/Te5y9aBwQbS5P7SpnIwro+lHP03uct2pMAg6dckHZR0SlLfO/ZJ2iTpKUmHJe3IlJ8n6UFJhyTdLmn5KO2x8Wlyf2lTLRSMfSZRrqUe/Tf1aXCjnjE8Dvwq8I1+FSQtA24BrgQuAK6VdEG6+hPAzRGxAXgZuG7E9lgB8n50mtxf2lT9gu7cmYPPJMqz2NF/6wJ1RIw8AfcDnT7rfhG4O7P80XQS8D3g9Lx6C02XXHJJ2Hjs2ROxYkVE8pOTTCtWRHz4w/nle/ZU3eL2mp7u3t9z07Jl+eXT01W3eDL1+87U8bsBzMQAv7FljDGsBZ7PLB9Ny84GfhARr/WUW4X6dV/cdVdz+0ubql/3xcmT+fXdrVeNNo6/LRoYJN0j6fGcafOA76GcsligvF87tkuakTQzOzs74FvbsBYaS2hqf2lT9eu+mJ7Or+9uvWq0cfxt0VtiRMRlI77HUeDczPI5wDGSbqSzJJ2enjXMlfdrx25gNySP9hyxTdaHb5dRL/1ucZG91QI0Jw2yjdr4nSmjK+khYEOagbQc2ALsS/u77gOuSettA+4soT22gCbnXk+KJqdBtlErvzODDET0m4BfITkjeBV4gXTwGPgZ4K5MvfcCTwPfAXZmys8HvgUcBr4EnDHI+3rwebz27EkGMqVkXsdBNLM6acp3hgEHn5XUbZZOpxMzMzNVN8PMrFEkPRwRfa85m+Mrn83MrIsDg5mZdXFgMDOzLg4MZmbWxYHBzMy6NDIrSdIskHNJyUBWk1xcV1d1bx/Uv411bx/Uv41u3+jq2MbpiJharFIjA8MoJM0Mkq5Vlbq3D+rfxrq3D+rfRrdvdE1oYz/uSjIzsy4ODGZm1mUSA8PuqhuwiLq3D+rfxrq3D+rfRrdvdE1oY66JG2MwM7OFTeIZg5mZLaC1gUHSJklPSTosaUfO+jMk3Z6uf1DS+hLbdq6k+yQ9KemgpH+TU+dSSa9IOpBOf1hW+zJtOCLpsfT95921UIk/Tffho5IuLrFtP5vZNwck/VDS7/bUKX0fSvqcpBclPZ4pWyVpv6RD6Xxln223pXUOSdpWYvv+RNLfpP+HX5F0Vp9tF/w8jLF9H5f0fzL/j+/ts+2C3/kxt/H2TPuOSDrQZ9ux78NCDHIL1qZNwDKSW3yfDywHHgEu6Knzr4A/T19vAW4vsX1rgIvT1z9Nckvy3vZdCvz3ivfjEWD1AuvfC3yV5Gl8bwcerPD/+29JcrQr3YfAO4GLgcczZX8M7Ehf7wA+kbPdKuCZdL4yfb2ypPZdwevPXv9EXvsG+TyMsX0fB35/gM/Agt/5cbaxZ/1/AP6wqn1YxNTWM4aNwOGIeCYifgzcBvQ+inQzcGv6+svAuyXlPW60cBFxPCK+nb7+v8CTNPN515uBL0TiAZIn8q2poB3vBr4TEUu96LEwEfEN4KWe4uxn7Vbg6pxN3wPsj4iXIuJlYD+wqYz2RcTX4vVnrz9A8jTFSvTZf4MY5DtfiIXamP6GvA/44jjeuyxtDQxrgeczy0eZ/8P7d3XSL8UrwNmltC4j7cK6CHgwZ/UvSnpE0lclva3UhiUC+JqkhyVtz1k/yH4uwxb6fxGr3ocAb46I45AcFABvyqlTl335QZKzwDyLfR7G6Ya0q+tzfbri6rL//jnwQkQc6rO+yn04sLYGhrwj/970q0HqjJWkM4H/AvxuRPywZ/W3SbpG/gnwZ8B/LbNtqXdExMXAlcBvS3pnz/o67MPlwFUkTwDsVYd9OKg67MudwGvA3j5VFvs8jMungLcCFwLHSbpqelW+/1LXsvDZQlX7cChtDQxHgXMzy+cAx/rVkXQ68EaWdgq7JJJ+iiQo7I2Iv+5dHxE/jIgfpa/vAn5K0uqy2pe+77F0/iLwFZLT9axB9vO4XQl8OyJe6F1Rh32YemGuiy2dv5hTp9J9mQ52/wtga6Sd4b0G+DyMRUS8EBEnI+IU8Bd93rfyz2L6O/KrwO396lS1D4fV1sDwELBB0nnpEeUWYF9PnX3AXObHNcDX+30hipb2Q34WeDIi/mOfOm+ZG/OQtJHk/+r7ZbQvfc+/J+mn516TDFA+3lNtH/D+NDvp7cArc10mJep7hFb1PszIfta2AXfm1LkbuELSyrSr5Iq0bOwkbQI+AlwVESf61Bnk8zCu9mXHrX6lz/sO8p0ft8uAv4mIo3krq9yHQ6t69HtcE0nGzNMkmQo707IbST78AG8g6X44DHwLOL/Etv0zktPcR4ED6fRe4Hrg+rTODcBBkuyKB4BfKnn/nZ++9yNpO+b2YbaNAm5J9/FjQKfkNq4g+aF/Y6as0n1IEqSOAz8hOYq9jmTs6l7gUDpfldbtAJ/JbPvB9PN4GPitEtt3mKR/fu6zOJet9zPAXQt9Hkpq31+ln69HSX7s1/S2L12e950vq41p+efnPnuZuqXvwyImX/lsZmZd2tqVZGZmS+TAYGZmXRwYzMysiwODmZl1cWAwM7MuDgxmZtbFgcHMzLo4MJiZWZf/D+fvIclkTzgxAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "x = np.linspace(0, 6 * np.pi, 50)\n", + "x = np.linspace(0, 20, 100)\n", "y = np.sin(x)\n", "plt.plot(x[y > 0], y[y > 0], 'bo')\n", "plt.plot(x[y <= 0], y[y <= 0], 'ro');" @@ -1221,12 +1239,14 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1291,7 +1311,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.8.2" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false }, "varInspector": { "cols": { @@ -1328,5 +1366,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/notebook3_for_and_if/py_exploratory_comp_3.ipynb b/notebook3_for_and_if/py_exploratory_comp_3.ipynb index 35a0866..428e4c6 100644 --- a/notebook3_for_and_if/py_exploratory_comp_3.ipynb +++ b/notebook3_for_and_if/py_exploratory_comp_3.ipynb @@ -46,14 +46,14 @@ "outputs": [], "source": [ "for i in [0, 1, 2, 3, 4]:\n", - " print('Hello world', i)" + " print('Hello world, the value of i is', i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In the code above, the variable `i` loops through the five values in the list `[0, 1, 2, 3, 4]`. The first time through, the value of `i` is equal to `0`, the second time through, its value is `1`, and so on till the last time when its value is `4`. Note the syntax of a `for` loop. At the end of the `for` statement you need to put a colon (`:`) and after that you need to indent. It doesn't matter how many spaces you indent, as long as you keep using the same number of spaces for the entire `for` loop. Jupyter Notebooks automatically indent 4 spaces, which is considered good Python style, so use that. You can have as many lines of code inside the `for` loop as you want. To end the `for` loop, simply stop indenting. " + "In the code above, the variable `i` loops through the five values in the list `[0, 1, 2, 3, 4]`. The first time through, the value of `i` is equal to `0`, the second time through, its value is `1`, and so on till the last time when its value is `4`. Note the syntax of a `for` loop: At the end of the `for` statement you need to put a colon (`:`) and after that you need to indent. It doesn't matter how many spaces you indent, as long as you keep using the same number of spaces for the entire `for` loop. Jupyter Notebooks automatically indent 4 spaces, which is considered good Python style, so use that. You can have as many lines of code inside the `for` loop as you want. To end the `for` loop, simply stop indenting. " ] }, { @@ -124,7 +124,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A loop can be used to fill an array. Let's compute $y=\\cos(x)$ where $x$ is an array that varies from 0 to $2\\pi$ with 100 points. We already know, of course, that this can be done with the statement `y=cos(x)`. Sometimes this is not possible, however, and we need to fill an array with a loop. First we have to create the array `y` (for example filled with zeros using the `zeros_like` function) and then fill it with the correct values by looping through all values of `x`, so that the index goes from `0` to the length of the `x` array. The counter in the loop (the variable `i` in the code below) is used as the index of the array that is filled." + "A loop can be used to fill an array. Let's compute $y=\\cos(x)$ where $x$ is an array that varies from 0 to $2\\pi$ with 100 points. We already know, of course, that this can be done with the statement `y = np.cos(x)`. Sometimes this is not possible, however, and we need to fill an array with a loop. First we have to create the array `y` (for example filled with zeros using the `zeros_like` function) and then fill it with the correct values by looping through all values of `x`, so that the index goes from `0` to the length of the `x` array. The counter in the loop (the variable `i` in the code below) is used as the index of the array that is filled." ] }, { @@ -144,7 +144,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Loops are very useful constructs in a programming script. Whenever you need to do a computation multiple times you should automatically think: *loop !*. " + "Loops are very useful constructs in a programming script. Whenever you need to do a computation multiple times you should automatically think: *loop!*. " ] }, { @@ -156,7 +156,7 @@ "\n", "`The number of days in MONTH is XX days`\n", "\n", - "where, of course, you print the correct name of the month for `MONTH` and the correct number of days for `XX`." + "where, of course, you print the correct name of the month for `MONTH` and the correct number of days for `XX`. Use f-strings." ] }, { @@ -203,7 +203,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice the syntax of the `if` statement. It starts with `if` followed by a statement that is either `True` or `False` and then a colon. After the colon, you need to indent and the entire indented code block (in this case 2 lines of code) is executed if the statement is `True`. The `if` statement is completed when you stop indenting. Recall from Notebook 2 that you can use larger than `>`, larger than or equal `>=`, equal `==`, smaller than or equal `<=`, smaller than `<` or not equal `!=`." + "Note the syntax of the `if` statement: It starts with `if` followed by a statement that is either `True` or `False` and then a colon. After the colon, you need to indent and the entire indented code block (in this case 2 lines of code) is executed if the statement is `True`. The `if` statement is completed when you stop indenting. Recall from Notebook 2 that you can use larger than `>`, larger than or equal `>=`, equal `==`, smaller than or equal `<=`, smaller than `<` or not equal `!=`." ] }, { @@ -262,7 +262,7 @@ "metadata": {}, "outputs": [], "source": [ - "for i in range(3):\n", + "for i in range(3): # do this 3 times\n", " a = float(input('Enter a value: '))\n", " if a < 4:\n", " print('the entered value is smaller than 4')\n", @@ -306,7 +306,7 @@ "metadata": {}, "source": [ "### Exercise 3. Load and loop through temperature data\n", - "Load the temperature data for Holland from the data file (`holland_temperature.dat`). Loop through all monthly temperatures and print a message that includes the month number and states whether the monthly average temperature is above or below 10 degrees" + "Load the temperature data for Holland from the data file `holland_temperature.dat`. Loop through all monthly temperatures and print a message that includes the month number and states whether the monthly average temperature is above or below 10 degrees" ] }, { @@ -328,7 +328,7 @@ "metadata": {}, "source": [ "### Looping and summation\n", - "One application of a loop is to compute the sum of all the values in an array. Consider, for example, the array `data` with 8 values. We will compute the sum of all values in `data`. We first define a variable `datasum` and assign it the initial value 0. Next, we loop through all the values in `data` and add the value to `datasum`:" + "One application of a loop is to compute the sum of all the values in an array. Consider, for example, the array `data` with 8 values. We will compute the sum of all values in `data`. We first define a variable `datasum` and assign it the initial value 0. Next, we loop through all the values in `data` and add each value to `datasum`:" ] }, { @@ -387,7 +387,7 @@ "metadata": {}, "source": [ "### Finding the maximum value the hard way\n", - "Next, let's find the maximum in the array `data` and the index of the maximum value. For illustration purposes, we will do this the hard way by using a loop and an if statement. First, we create a variable `maxvalue` that contains the maximum value and set it initially to a very small number, and we create a variable `maxindex` that is the index of the maximum value and is initially set to `None`. Then we loop through all values in `data` and update the `maxvalue` and `maxindex` everytime we find a larger value than the current `maxvalue`" + "Next, let's find the maximum in the array `data` and the index of the maximum value. For illustration purposes, we will do this the hard way by using a loop and an if statement. First, we create a variable `maxvalue` that contains the maximum value and set it initially to a very small number, and we create a variable `maxindex` that is the index of the maximum value and is initially set to `None`. Next we loop through all values in `data` and update the `maxvalue` and `maxindex` everytime we find a larger value than the current `maxvalue`" ] }, { @@ -487,8 +487,8 @@ "outputs": [], "source": [ "print('sum of entire array:', np.sum(data))\n", - "print('sum the rows (axis=0):', np.sum(data, axis=0))\n", - "print('sum the columns (axis=1):', np.sum(data, axis=1))" + "print('sum rows (axis=0):', np.sum(data, axis=0))\n", + "print('sum columns (axis=1):', np.sum(data, axis=1))" ] }, { @@ -520,7 +520,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "There is another way of coding this using a `while` loop as shown below" + "There is another way to code this using a `while` loop as shown below" ] }, { @@ -541,7 +541,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the `while` loop, the comparison is done at the beginning of the loop, while the counter (in this case `i`) is updated inside the loop. Either a loop with a `break` or a `while` loop with a counter works fine, but `while` loops may be tricky in some cases, as they can result in infinite loops when you have an error in your code. Once you are in an infinite loop (one that never stops), click on the [Kernel] menu item at the top of the window and select [Restart]. This will end your Python session and start a new one. When you print something to the screen in your `while` loop, it may not be possible to break out of the loop and you may need to end your Jupyter session (and potentially lose some of your work). Because of these problems with errors in `while` loops, it is recommended to use a loop with a break rather than a while loop when possible. " + "In the `while` loop, the comparison is done at the beginning of the loop, while the counter (in this case `i`) is updated inside the loop. Either a loop with a `break` or a `while` loop with a counter works fine, but `while` loops may be tricky in some cases, as they can result in infinite loops when you have an error in your code. Once you are in an infinite loop (one that never stops), click on the [Kernel] menu item at the top of the window and select [Interrupt Kernel] or [Restart Kernel]. This will end your Python session and start a new one. When you print something to the screen in your `while` loop, it may not be possible to break out of the loop and you may need to end your Jupyter session (and potentially lose some of your work). Because of these problems with errors in `while` loops, it is recommended to use a loop with a break rather than a while loop when possible. " ] }, { @@ -591,7 +591,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "When you add two strings, they are put back to back, just like lists. When you want to combine text with a variable, you first need to change the variable to a string and then add the two strings" + "When you add two strings, they are put back to back, just like lists. When you want to combine text with a variable, you first need to change the variable to a string and then add the two strings:" ] }, { @@ -612,7 +612,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "When text and a variable are combined in, for example, the title of a figure, the text and variable have to be combined into a string, which is then passed as a title to the graph" + "Comparisons work on strings just like they work on numbers. The comparison starts with the first character in a string and only goes to the next character when the first characters of both strings are equal. The letter 'a' is smaller than 'b', 'b' is smaller than 'c', etc. But be careful, in the order of things, the upper case characters are smaller than all lower case characters! So 'A' is smaller than 'a', but also smaller than 'm' or any other lower case character. Make sure you understand the following statements" ] }, { @@ -620,26 +620,6 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "a = 77\n", - "plt.plot([1, 3, 2])\n", - "plt.title('the value of a is ' + str(a));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Comparisons work on strings just like they work on numbers. The comparison starts with the first character in a string and only goes to the next character when the first characters of both strings are equal. The letter 'a' is smaller than 'b', 'b' is smaller than 'c', etc. But be careful, in the order of things, the upper case characters are smaller than all lower case characters! So 'A' is smaller than 'a', but also than 'm' or any other lower case character. Make sure you understand the following statements" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], "source": [ "print('delft' < 'eindhoven') # True as 'd' is smaller than 'e'\n", "print('dalft' < 'delft') # True as 'a' is smaller than 'e'\n", @@ -671,7 +651,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A string conisting of multiple words can be converted into a list of words using `split`" + "A string consisting of multiple words can be converted into a list of words using `split`" ] }, { @@ -681,11 +661,9 @@ "outputs": [], "source": [ "sentence = 'This is a sentence containing a number of words'\n", - "print('This is the sentence:')\n", - "print(sentence)\n", + "print('This is the sentence:', sentence)\n", "wordlist = sentence.split()\n", - "print('This is the split sentence:')\n", - "print(wordlist)\n", + "print('This is the split sentence:', wordlist)\n", "print('All words may be printed seperately:')\n", "for word in wordlist:\n", " print(word)" @@ -739,7 +717,7 @@ " 'October', 'November', 'December']\n", "days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n", "for i in range(12):\n", - " print('The number of days in', months[i], 'is', days[i])" + " print(f'The number of days in {months[i]} is {days[i]}')" ] }, { @@ -869,8 +847,8 @@ "for price in [40, 60, 80]:\n", " for i in range(nrow):\n", " if oilprice[i, 2] > price:\n", - " print('The oil price exceeds ', price, 'euros for the first time in', \\\n", - " months[int(oilprice[i, 1])], 'of', int(oilprice[i, 0]))\n", + " print(f'The oil price exceeds {price} euros for the first time in', \\\n", + " f'{months[int(oilprice[i, 1])]} of {oilprice[i, 0]:.0f}')\n", " break" ] }, @@ -921,7 +899,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.8.2" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false }, "varInspector": { "cols": { @@ -954,5 +950,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/notebook3_for_and_if/py_exploratory_comp_3_sol.ipynb b/notebook3_for_and_if/py_exploratory_comp_3_sol.ipynb index e6a7fb4..186cd18 100644 --- a/notebook3_for_and_if/py_exploratory_comp_3_sol.ipynb +++ b/notebook3_for_and_if/py_exploratory_comp_3_sol.ipynb @@ -48,24 +48,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "Hello world 0\n", - "Hello world 1\n", - "Hello world 2\n", - "Hello world 3\n", - "Hello world 4\n" + "Hello world, the value of i is 0\n", + "Hello world, the value of i is 1\n", + "Hello world, the value of i is 2\n", + "Hello world, the value of i is 3\n", + "Hello world, the value of i is 4\n" ] } ], "source": [ "for i in [0, 1, 2, 3, 4]:\n", - " print('Hello world', i)" + " print('Hello world, the value of i is', i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In the code above, the variable `i` loops through the five values in the list `[0, 1, 2, 3, 4]`. The first time through, the value of `i` is equal to `0`, the second time through, its value is `1`, and so on till the last time when its value is `4`. Note the syntax of a `for` loop. At the end of the `for` statement you need to put a colon (`:`) and after that you need to indent. It doesn't matter how many spaces you indent, as long as you keep using the same number of spaces for the entire `for` loop. Jupyter Notebooks automatically indent 4 spaces, which is considered good Python style, so use that. You can have as many lines of code inside the `for` loop as you want. To end the `for` loop, simply stop indenting. " + "In the code above, the variable `i` loops through the five values in the list `[0, 1, 2, 3, 4]`. The first time through, the value of `i` is equal to `0`, the second time through, its value is `1`, and so on till the last time when its value is `4`. Note the syntax of a `for` loop: At the end of the `for` statement you need to put a colon (`:`) and after that you need to indent. It doesn't matter how many spaces you indent, as long as you keep using the same number of spaces for the entire `for` loop. Jupyter Notebooks automatically indent 4 spaces, which is considered good Python style, so use that. You can have as many lines of code inside the `for` loop as you want. To end the `for` loop, simply stop indenting. " ] }, { @@ -185,7 +185,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A loop can be used to fill an array. Let's compute $y=\\cos(x)$ where $x$ is an array that varies from 0 to $2\\pi$ with 100 points. We already know, of course, that this can be done with the statement `y=cos(x)`. Sometimes this is not possible, however, and we need to fill an array with a loop. First we have to create the array `y` (for example filled with zeros using the `zeros_like` function) and then fill it with the correct values by looping through all values of `x`, so that the index goes from `0` to the length of the `x` array. The counter in the loop (the variable `i` in the code below) is used as the index of the array that is filled." + "A loop can be used to fill an array. Let's compute $y=\\cos(x)$ where $x$ is an array that varies from 0 to $2\\pi$ with 100 points. We already know, of course, that this can be done with the statement `y = np.cos(x)`. Sometimes this is not possible, however, and we need to fill an array with a loop. First we have to create the array `y` (for example filled with zeros using the `zeros_like` function) and then fill it with the correct values by looping through all values of `x`, so that the index goes from `0` to the length of the `x` array. The counter in the loop (the variable `i` in the code below) is used as the index of the array that is filled." ] }, { @@ -195,12 +195,14 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -216,7 +218,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Loops are very useful constructs in a programming script. Whenever you need to do a computation multiple times you should automatically think: *loop !*. " + "Loops are very useful constructs in a programming script. Whenever you need to do a computation multiple times you should automatically think: *loop!*. " ] }, { @@ -228,7 +230,7 @@ "\n", "`The number of days in MONTH is XX days`\n", "\n", - "where, of course, you print the correct name of the month for `MONTH` and the correct number of days for `XX`." + "where, of course, you print the correct name of the month for `MONTH` and the correct number of days for `XX`. Use f-strings." ] }, { @@ -286,7 +288,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice the syntax of the `if` statement. It starts with `if` followed by a statement that is either `True` or `False` and then a colon. After the colon, you need to indent and the entire indented code block (in this case 2 lines of code) is executed if the statement is `True`. The `if` statement is completed when you stop indenting. Recall from Notebook 2 that you can use larger than `>`, larger than or equal `>=`, equal `==`, smaller than or equal `<=`, smaller than `<` or not equal `!=`." + "Note the syntax of the `if` statement: It starts with `if` followed by a statement that is either `True` or `False` and then a colon. After the colon, you need to indent and the entire indented code block (in this case 2 lines of code) is executed if the statement is `True`. The `if` statement is completed when you stop indenting. Recall from Notebook 2 that you can use larger than `>`, larger than or equal `>=`, equal `==`, smaller than or equal `<=`, smaller than `<` or not equal `!=`." ] }, { @@ -360,21 +362,51 @@ "execution_count": 11, "metadata": {}, "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter a value: 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the entered value is smaller than 4\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter a value: 7\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Enter a value: 3\n", - "the entered value is smaller than 4\n", - "Enter a value: 4\n", - "the entered value is equal to 4\n", - "Enter a value: 5\n", "the entered value is larger than 4\n" ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter a value: 4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the entered value is equal to 4\n" + ] } ], "source": [ - "for i in range(3):\n", + "for i in range(3): # do this 3 times\n", " a = float(input('Enter a value: '))\n", " if a < 4:\n", " print('the entered value is smaller than 4')\n", @@ -418,7 +450,7 @@ "metadata": {}, "source": [ "### Exercise 3. Load and loop through temperature data\n", - "Load the temperature data for Holland from the data file (`holland_temperature.dat`). Loop through all monthly temperatures and print a message that includes the month number and states whether the monthly average temperature is above or below 10 degrees" + "Load the temperature data for Holland from the data file `holland_temperature.dat`. Loop through all monthly temperatures and print a message that includes the month number and states whether the monthly average temperature is above or below 10 degrees" ] }, { @@ -440,12 +472,12 @@ "metadata": {}, "source": [ "### Looping and summation\n", - "One application of a loop is to compute the sum of all the values in an array. Consider, for example, the array `data` with 8 values. We will compute the sum of all values in `data`. We first define a variable `datasum` and assign it the initial value 0. Next, we loop through all the values in `data` and add the value to `datasum`:" + "One application of a loop is to compute the sum of all the values in an array. Consider, for example, the array `data` with 8 values. We will compute the sum of all values in `data`. We first define a variable `datasum` and assign it the initial value 0. Next, we loop through all the values in `data` and add each value to `datasum`:" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -515,12 +547,12 @@ "metadata": {}, "source": [ "### Finding the maximum value the hard way\n", - "Next, let's find the maximum in the array `data` and the index of the maximum value. For illustration purposes, we will do this the hard way by using a loop and an if statement. First, we create a variable `maxvalue` that contains the maximum value and set it initially to a very small number, and we create a variable `maxindex` that is the index of the maximum value and is initially set to `None`. Then we loop through all values in `data` and update the `maxvalue` and `maxindex` everytime we find a larger value than the current `maxvalue`" + "Next, let's find the maximum in the array `data` and the index of the maximum value. For illustration purposes, we will do this the hard way by using a loop and an if statement. First, we create a variable `maxvalue` that contains the maximum value and set it initially to a very small number, and we create a variable `maxindex` that is the index of the maximum value and is initially set to `None`. Next we loop through all values in `data` and update the `maxvalue` and `maxindex` everytime we find a larger value than the current `maxvalue`" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -552,7 +584,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -601,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -636,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -644,15 +676,15 @@ "output_type": "stream", "text": [ "sum of entire array: 51\n", - "sum the rows (axis=0): [ 8 15 13 15]\n", - "sum the columns (axis=1): [11 22 18]\n" + "sum rows (axis=0): [ 8 15 13 15]\n", + "sum columns (axis=1): [11 22 18]\n" ] } ], "source": [ "print('sum of entire array:', np.sum(data))\n", - "print('sum the rows (axis=0):', np.sum(data, axis=0))\n", - "print('sum the columns (axis=1):', np.sum(data, axis=1))" + "print('sum rows (axis=0):', np.sum(data, axis=0))\n", + "print('sum columns (axis=1):', np.sum(data, axis=1))" ] }, { @@ -668,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -692,12 +724,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "There is another way of coding this using a `while` loop as shown below" + "There is another way to code this using a `while` loop as shown below" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -721,7 +753,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the `while` loop, the comparison is done at the beginning of the loop, while the counter (in this case `i`) is updated inside the loop. Either a loop with a `break` or a `while` loop with a counter works fine, but `while` loops may be tricky in some cases, as they can result in infinite loops when you have an error in your code. Once you are in an infinite loop (one that never stops), click on the [Kernel] menu item at the top of the window and select [Restart]. This will end your Python session and start a new one. When you print something to the screen in your `while` loop, it may not be possible to break out of the loop and you may need to end your Jupyter session (and potentially lose some of your work). Because of these problems with errors in `while` loops, it is recommended to use a loop with a break rather than a while loop when possible. " + "In the `while` loop, the comparison is done at the beginning of the loop, while the counter (in this case `i`) is updated inside the loop. Either a loop with a `break` or a `while` loop with a counter works fine, but `while` loops may be tricky in some cases, as they can result in infinite loops when you have an error in your code. Once you are in an infinite loop (one that never stops), click on the [Kernel] menu item at the top of the window and select [Interrupt Kernel] or [Restart Kernel]. This will end your Python session and start a new one. When you print something to the screen in your `while` loop, it may not be possible to break out of the loop and you may need to end your Jupyter session (and potentially lose some of your work). Because of these problems with errors in `while` loops, it is recommended to use a loop with a break rather than a while loop when possible. " ] }, { @@ -757,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -781,12 +813,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "When you add two strings, they are put back to back, just like lists. When you want to combine text with a variable, you first need to change the variable to a string and then add the two strings" + "When you add two strings, they are put back to back, just like lists. When you want to combine text with a variable, you first need to change the variable to a string and then add the two strings:" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -811,44 +843,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "When text and a variable are combined in, for example, the title of a figure, the text and variable have to be combined into a string, which is then passed as a title to the graph" + "Comparisons work on strings just like they work on numbers. The comparison starts with the first character in a string and only goes to the next character when the first characters of both strings are equal. The letter 'a' is smaller than 'b', 'b' is smaller than 'c', etc. But be careful, in the order of things, the upper case characters are smaller than all lower case characters! So 'A' is smaller than 'a', but also smaller than 'm' or any other lower case character. Make sure you understand the following statements" ] }, { "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = 77\n", - "plt.plot([1, 3, 2])\n", - "plt.title('the value of a is ' + str(a));" - ] - }, - { - "cell_type": "markdown", + "execution_count": 21, "metadata": {}, - "source": [ - "Comparisons work on strings just like they work on numbers. The comparison starts with the first character in a string and only goes to the next character when the first characters of both strings are equal. The letter 'a' is smaller than 'b', 'b' is smaller than 'c', etc. But be careful, in the order of things, the upper case characters are smaller than all lower case characters! So 'A' is smaller than 'a', but also than 'm' or any other lower case character. Make sure you understand the following statements" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "scrolled": true - }, "outputs": [ { "name": "stdout", @@ -879,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -903,22 +904,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A string conisting of multiple words can be converted into a list of words using `split`" + "A string consisting of multiple words can be converted into a list of words using `split`" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "This is the sentence:\n", - "This is a sentence containing a number of words\n", - "This is the split sentence:\n", - "['This', 'is', 'a', 'sentence', 'containing', 'a', 'number', 'of', 'words']\n", + "This is the sentence: This is a sentence containing a number of words\n", + "This is the split sentence: ['This', 'is', 'a', 'sentence', 'containing', 'a', 'number', 'of', 'words']\n", "All words may be printed seperately:\n", "This\n", "is\n", @@ -934,11 +933,9 @@ ], "source": [ "sentence = 'This is a sentence containing a number of words'\n", - "print('This is the sentence:')\n", - "print(sentence)\n", + "print('This is the sentence:', sentence)\n", "wordlist = sentence.split()\n", - "print('This is the split sentence:')\n", - "print(wordlist)\n", + "print('This is the split sentence:', wordlist)\n", "print('All words may be printed seperately:')\n", "for word in wordlist:\n", " print(word)" @@ -983,7 +980,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1011,7 +1008,7 @@ " 'October', 'November', 'December']\n", "days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n", "for i in range(12):\n", - " print('The number of days in', months[i], 'is', days[i])" + " print(f'The number of days in {months[i]} is {days[i]}')" ] }, { @@ -1025,17 +1022,19 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1062,7 +1061,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1104,7 +1103,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1139,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1179,26 +1178,28 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The oil price exceeds 40 euros for the first time in April of 2005\n", - "The oil price exceeds 60 euros for the first time in December of 2007\n", - "The oil price exceeds 80 euros for the first time in June of 2008\n" + "The oil price exceeds 40 euros for the first time in April of 2005\n", + "The oil price exceeds 60 euros for the first time in December of 2007\n", + "The oil price exceeds 80 euros for the first time in June of 2008\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1212,8 +1213,8 @@ "for price in [40, 60, 80]:\n", " for i in range(nrow):\n", " if oilprice[i, 2] > price:\n", - " print('The oil price exceeds ', price, 'euros for the first time in', \\\n", - " months[int(oilprice[i, 1])], 'of', int(oilprice[i, 0]))\n", + " print(f'The oil price exceeds {price} euros for the first time in', \\\n", + " f'{months[int(oilprice[i, 1])]} of {oilprice[i, 0]:.0f}')\n", " break" ] }, @@ -1228,7 +1229,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1272,7 +1273,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.8.2" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false }, "varInspector": { "cols": { @@ -1305,5 +1324,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/notebook4_functions/py_exploratory_comp_4.ipynb b/notebook4_functions/py_exploratory_comp_4.ipynb index 03ccb28..4767977 100644 --- a/notebook4_functions/py_exploratory_comp_4.ipynb +++ b/notebook4_functions/py_exploratory_comp_4.ipynb @@ -33,13 +33,13 @@ "\n", "`import numpy as np`\n", "\n", - "all functions in `numpy` can be called as `np.function()`. \n", + "after which all functions in `numpy` can be called as `np.function()`. \n", "\n", "Packages can also have subpackages. For example, the `numpy` package has a subpackage called `random`, which has a bunch of functions to deal with random variables. If the `numpy` package is imported with `import numpy as np`, functions in the `random` subpackage can be called as `np.random.function()`. \n", "\n", - "If you only need one specific function, you don't have to import the entire package. For example, if you only want the cosine function of the numpy package, you may import it as `from numpy import cos`, after which you can simply call the cosine function as `cos()`. You can even rename functions when you import them. For example, after `from numpy import cos as newname`, you can call the function `newname()` to compute the cosine (I know, pretty silly, but this is can become handy). \n", + "If you only need one specific function, you don't have to import the entire package. For example, if you only want the cosine function of the numpy package, you may import it as `from numpy import cos`, after which you can simply call the cosine function as `cos()`. You can even rename functions when you import them. For example, after `from numpy import cos as newname`, you can call the function `newname()` to compute the cosine (I know, pretty silly, but this can become handy). \n", "\n", - "In the previous Notebooks we always imported `numpy` and called it `np` and we imported the plotting part of `matplotlib` and called it `plt`. Both are standard names in the Python community. The statement we added before importing `matplotlib` is `%matplotlib inline`. This latter command is an IPython command and not a Python command. It will only work in IPython and is called a magic command. All magic commands are preceded with a `%`. The statement `%matplotlib inline` puts all figures in the Notebook rather than in a separate window. \n", + "In the previous Notebooks we always imported `numpy` and called it `np` and we imported the `matplotlib.pyplot` and called it `plt`. Both are standard names in the Python community. The statement we added before importing `matplotlib` is `%matplotlib inline`. This latter command is an IPython command and not a Python command. It will only work in IPython and is called a magic command. All magic commands are preceded with a `%`. The statement `%matplotlib inline` puts all figures in the Notebook rather than in a separate window. \n", "\n", "Enough about packages for now. Let's start the way we always start." ] @@ -61,7 +61,7 @@ "source": [ "### Functions\n", "Functions are an essential part of a programming language.\n", - "You already used many functions like `plot`, `loadtxt`, or `linspace`.\n", + "You already used many functions like `plot`, `loadtxt`, and `linspace`.\n", "But you can also define your own functions.\n", "To define a new function, use the `def` command. After `def` follows the name of the function and then between parentheses the arguments of the function and finally a colon. After the colon you indent until you are done with the function. The last line of the function should be `return` followed by what you want to return. For example, consider the following function of $x$:\n", "\n", @@ -84,6 +84,7 @@ " else:\n", " f = np.exp(-x)\n", " return f\n", + "\n", "print(func(3))" ] }, @@ -111,11 +112,11 @@ "\n", "`func(` and then hit [shift-tab]\n", "\n", - "and wait a split second, the input arguments of the function pop-up in a little window, just like for other functions we already used. You can also provide additional documentation of your function. Put the documentation at the top of the indented block and put it between triple double quotes (`\"\"\"`). Run the code below to define the function `func` with the additional documentation, then in the code cell below that type \n", + "and wait a split second, the input arguments of the function pop-up in a little window, just like for other functions we already used. You can also provide additional documentation of your function. Put the documentation at the top of the indented block and put it between triple double quotes (`\"\"\"`). Run the code below to define the function `func` with the additional documentation, then in the code cell below type \n", "\n", "`func(` \n", "\n", - "and hit [shift][tab] to see the additional documentation. Warning: don't leave a code cell with just `func(` or `func()` as you will get an error on [Kernel][Restart & Run All]." + "and hit [shift][tab] to see the additional documentation. Warning: don't leave a code cell with just `func(` or `func()` as you will get an error on [Kernel][Restart & Run All Cells]." ] }, { @@ -145,7 +146,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The names of the arguments of a function are the names used inside the function. They have no relationship to the names used outside the function. When using a variable as the argument of a function, only the *value* gets passed to the function. In the example below, the *value* of `y` is passed as the first argument ot the function `func`. Inside the function, this value is used for the variable `x`." + "The names of the arguments of a function are the names used inside the function. They have no relationship to the names used outside the function. When using a variable as the argument of a function, only the *value* gets passed to the function. In the example below, the *value* of `y` is passed as the first argument of the function `func`. Inside the function, this value is used for the variable `x`." ] }, { @@ -167,7 +168,7 @@ "\n", "$f(x)=e^{-\\alpha x}\\cos(x)$\n", "\n", - "The function should take `x` and `alpha` as input arguments and return the function value. Give your function a unique name (if you also call it `func` it will overwrite the `func` function that we defined above). Make a plot of `f` vs. `x` for `x` going from 0 to $10\\pi$ using two different values of `alpha`: 0.1 and 0.2. Add a legend and label the axes." + "The function should take `x` and `alpha` as input arguments and return the function value. Give your function a unique name (if you also call it `func` it will overwrite the `func` function that we defined above). Make a plot of $f(x)$ vs. $x$ for $x$ going from 0 to $10\\pi$ using two different values of $\\alpha$: 0.1 and 0.2. Add a legend and label the axes." ] }, { @@ -195,26 +196,32 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "def testfunc(x, A=1, theta=0):\n", " return A * np.cos(np.pi * x + theta)\n", + "\n", "print(testfunc(1)) # Uses default A=1, theta=0: cos(pi)\n", "print(testfunc(1, A=2)) # Now A=2, and theta is still 0: 2*cos(pi)\n", - "print(testfunc(1, A=2, theta=np.pi / 4)) # Now A=2, theta=pi/4: 2*cos(5pi/4) \n", + "print(testfunc(1, A=2, theta=np.pi / 4)) # Now A=2, theta=pi/4: 2*cos(5pi/4)\n", "print(testfunc(1, theta=np.pi / 4, A=2)) # Same as above: 2*cos(5pi/4)\n", "print(testfunc(1, theta=np.pi / 4)) # Now theta=pi/4, and A is still 1: cos(5pi/4)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the proper style was applied, as defined in Notebook 1: there are spaces around mathematical symbols, but not around the equal sign of the keyword argument. " + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Local variables\n", - "Variables declared inside a function can only be used inside that function. The outside of a function doesn't know about the variables used inside the function, except for the variables that are returned by the function. In the code below, remove the `#` before `print(a)` and you will get an error message, as `a` is a local variable inside the function `localtest` (then put the `#` back, else you get an error when running [Kernel][Restart & Run All])." + "Variables declared inside a function can only be used inside that function. The outside of a function doesn't know about the variables used inside the function, except for the variables that are returned by the function. In the code below, remove the `#` before `print(a)` and you will get an error message, as `a` is a local variable inside the function `localtest` (then put the `#` back, else you get an error when running [Kernel][Restart & Run All Cells])." ] }, { @@ -252,14 +259,17 @@ " a = 3\n", " b = 5\n", " return a * x + b\n", + "\n", "print(test1(4))\n", "\n", "# This function also works, but it is sloppy coding\n", "# since variable a is defined outside the function\n", "a = 3\n", + "\n", "def test2(x):\n", " b = 5\n", " return a * x + b\n", + "\n", "print(test2(4)) " ] }, @@ -277,9 +287,11 @@ "outputs": [], "source": [ "var1 = 8\n", + "\n", "def test3():\n", " var1 = 4\n", - " print('Inside the functino test3, var1 equals:', var1)\n", + " print('Inside the function test3, var1 equals:', var1)\n", + " \n", "test3()\n", "print('Value of var1 outside test3:', var1)" ] @@ -302,13 +314,13 @@ "The stream function is a function that is constant along stream lines. \n", "The stream function $\\psi$ is a function of polar coordinates $r$ and $\\theta$. The stream function is constant and equal to zero on the cylinder and doesn't really exist inside the cylinder, so let's make it zero there, like it is on the cylinder.\n", "\n", - "$\\begin{split}\n", + "$$\\begin{split}\n", "\\psi &= 0 \\qquad r\\le R \\\\\n", "\\psi &= U(r-R^2/r)\\sin(\\theta) \\qquad r\\ge R\n", - "\\end{split}$\n", + "\\end{split}$$\n", "\n", "where $U$ is the flow in the $x$-direction, $r$ is the radial distance from the center of the cylinder, $\\theta$ is the angle, and $R$ is the radius of the cylinder. You may recall it is not always easy to compute the correct angle when given a value of $x$ and $y$, as the regular arctan function returns a value between $-\\pi/2$ and $+\\pi/2$ (radians), while if $x=-2$ and $y=2$, the angle should be $3\\pi/4$.\n", - "`numpy` has a very cool function to compute the correct angle between $-\\pi$ and $+\\pi$ given the $x$ and $y$ coordinates. The function is `arctan2(y,x)`. Note that the function takes as its *first* argument `y` and as its *second* argument `x`. \n", + "`numpy` has a very cool function to compute the correct angle between $-\\pi$ and $+\\pi$ given the $x$ and $y$ coordinates. The function is `arctan2(y, x)`. Note that the function takes as its *first* argument `y` and as its *second* argument `x`. \n", "\n", "Write a function that computes the stream function for flow around a cylinder. The function should take two arguments, `x` and `y`, and two keyword arguments, `U` and `R`, and should return the stream function value. If you write the function correctly, it should give `psi(2, 4, U=2, R=1.5) = 7.1`, and `psi(0.5, 0, U=2, R=1.5) = 0` (inside the cylinder)." ] @@ -347,6 +359,7 @@ " else:\n", " f = np.exp(-x)\n", " return f\n", + "\n", "x = np.linspace(-6, 6, 100)\n", "#y = func(x) # Run this line after removing the # to see the error that occurs. Then put the # back" ] @@ -363,7 +376,7 @@ "\n", "`The truth value of an array with more than one element is ambiguous` \n", "\n", - "For some values of `x` the `if` statement may be `True`, for others it may be `False`. A simple way around this problem is to vectorize the function. That means we create a new function, let's call it `funcvec`, that is a vectorized form of `func` and can be called with an array as an argument (this is by far the easiest but not necessarily the computationally fastest way to make sure a function can be called with an array as an argument)" + "For some values of `x` the `if` statement may be `True`, for others it may be `False`. A simple way around this problem is to vectorize the function. That means we create a new function, let's call it `funcvec`, that is a vectorized form of `func` and can be called with an array as an argument. This is by far the easiest but not necessarily the computationally fastest way to make sure a function can be called with an array as an argument, and, unfortunately, it won't work for all situations. " ] }, { @@ -382,7 +395,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Back now to the problem of flow around a clinder. Contours of the stream function represent stream lines around the cylinder. To make a contour plot, the function to be contoured needs to be evaluated on a grid of points. The grid of points and an array with the values of the stream function at these points can be passed to a contouring routine to create a contour plot. To create a grid of points, use the function `meshgrid` which takes as input a range of `x` values and a range of `y` values, and returns a grid of `x` values and a grid of `y` values. For example, to have 5 points in the $x$-direction from -1 to +1, and 3 points in y-direction from 0 to 10:" + "Back now to the problem of flow around a clinder. Contours of the stream function represent stream lines around the cylinder. To make a contour plot, the function to be contoured needs to be evaluated on a grid of points. The grid of points and an array with the values of the stream function at these points can be passed to a contouring routine to create a contour plot. To create a grid of points, use the function `meshgrid` which takes as input an array of `x` values and an array of `y` values, and returns a grid of `x` values and a grid of `y` values. For example, to have 5 points in the $x$-direction from -1 to +1, and 3 points in y-direction from 0 to 10:" ] }, { @@ -391,7 +404,7 @@ "metadata": {}, "outputs": [], "source": [ - "x,y = np.meshgrid( np.linspace(-1,1,5), np.linspace(0,10,3) ) \n", + "x,y = np.meshgrid(np.linspace(-1, 1, 5), np.linspace(0, 10, 3)) \n", "print('x values')\n", "print(x)\n", "print('y values')\n", @@ -403,7 +416,7 @@ "metadata": {}, "source": [ "### Exercise 3, Contour plot for flow around a cylinder\n", - "Evaluate the function for the stream function around a cylinder with radius 1.5 on a grid of 100 by 100 points, where `x` varies from -4 to +4, and `y` varies from -3 to 3; use $U=1$. Evaluate the stream function on the entire grid (you need to create a vectorized version of the function you wrote to compute the stream function). Then use the `np.contour` function to create a contour plot (find out how by reading the help of the `contour` function or go to [this demo](http://matplotlib.org/examples/pylab_examples/contour_demo.html)) of the `matplotlib` gallery. You need to use the command `plt.axis('equal')`, so that the scales along the axes are equal and the circle looks like a circle rather than an ellipse. Finally, you may want to add a nice circular patch using the `fill` command and specifying a bunch of $x$ and $y$ values around the circumference of the cylinder." + "Evaluate the function for the stream function around a cylinder with radius 1.5 on a grid of 100 by 100 points, where `x` varies from -4 to +4, and `y` varies from -3 to 3; use $U=1$. Evaluate the stream function on the entire grid (you need to create a vectorized version of the function you wrote to compute the stream function). Then use the `np.contour` function to create a contour plot (find out how by reading the help of the `contour` function or go to [this demo](http://matplotlib.org/examples/pylab_examples/contour_demo.html)) of the `matplotlib` gallery). You need to use the command `plt.axis('equal')`, so that the scales along the axes are equal and the circle looks like a circle rather than an ellipse. Finally, you may want to add a nice circular patch using the `fill` command and specifying a bunch of $x$ and $y$ values around the circumference of the cylinder." ] }, { @@ -437,10 +450,12 @@ "a, b = 4, 3\n", "print('a:', a)\n", "print('b:', b)\n", + "\n", "a, b, c = 27, np.arange(4), 'hello'\n", "print('a:', a)\n", "print('b:', b)\n", "print('c:', c)\n", + "\n", "d, e, f = np.arange(0, 11, 5)\n", "print('d:', d)\n", "print('e:', e)\n", @@ -451,7 +466,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Similarly, a function may return one value or one array. Of a function may return multiple values, multiple arrays, or whatever the programmer decides to return (including nothing, of course). When multiple *things* are returned, they are returned as a tuple. They can be stored as a tuple, or, if the user knows how many *things* are returned, they can be stored in individual variables right away, as in the example above." + "Similarly, a function may return one value or one array. Or a function may return multiple values, multiple arrays, or whatever the programmer decides to return (including nothing, of course). When multiple *things* are returned, they are returned as a tuple. They can be stored as a tuple, or, if the user knows how many *things* are returned, they can be stored in individual variables right away, as in the example below." ] }, { @@ -464,9 +479,11 @@ " dump = 4 * np.ones(5)\n", " dump[0] = 100\n", " return 33, dump, 'this works great!'\n", + "\n", "test = newfunc()\n", "print(type(test))\n", "print(test[1]) \n", + "\n", "a, b, c = newfunc()\n", "print('a:', a)\n", "print('b:', b)\n", @@ -582,10 +599,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Finding the zero of a function\n", - "Finding the zero of a function is a common task in exploratory computing. The value where the function equals zero is also called the *root* and finding the zero is referred to as *root finding*. There exist a number of methods to find the zero of a function varying from robust but slow (so it always finds a zero but it takes quite a few function evaluations) to fast but not so robust (it can find the zero very fast, but it won't always find it). Here we'll use the latter one.\n", + "### Exercise 6. Numerical integration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Numerical integration of a function is a common engineering task. \n", + "The `scipy` package has a specific subpackage called `integrate` with a number of numerical integration functions. We will use the `quad` function. Use the `quad` function to integrate the function $f(x)=\\text{e}^{-x}$ from 1 till 5 (note that `quad` returns two values; read the doc string to find out what they are). Check that you did it right by doing the integration by hand (which is easy for this function). \n", + "\n", + "Next, compute the following integral:\n", + "\n", + "$$\\int_1^5 \\frac{\\text{e}^{-x}}{x}\\text{d}x$$ \n", "\n", - "Consider the function $f(x)=0.5-\\text{e}^{-x}$. The function is zero when $x=-\\ln(0.5)$, but let's pretend we don't know that and try to find it using a root finding method. First, we need to write a Python function for $f(x)$." + "This integral is more difficult to do analytically. Perform the integration numerically with the `quad` function and check your answer, for example, at the [wolframalpha website](https://www.wolframalpha.com) where you can simply type: `integrate exp(-x)/x from 1 to 5`." ] }, { @@ -593,16 +621,23 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "def f(x):\n", - " return 0.5 - np.exp(-x)" + "Answer to Exercise 6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We will use the method `fsolve` to find the zero of a function. `fsolve` is part of the `scipy.optimize` package. `fsolve` takes two arguments: the function for which we want to find the zero, and a starting value for the search (not surpisingly, the closer the starting value is to the root, the higher the chance that `fsolve` will find it)." + "### Interactive functions using `ipywidgets`\n", + "The package `ipywidgets` constains widgets for use in Jupyter Notebooks. We will start here by using the simplest form, which is the `interact` function. The `interact` function can be used to, you guessed it, interact with a function. The `interact` function is a bit slow when interacting with a graph, but other than that it works nicely. \n", + "\n", + "For example, let's write a function that plots a line with length $L$ that makes an angle $\\alpha$ with the horizontal. The angle $\\alpha$ is an input argument." ] }, { @@ -611,18 +646,23 @@ "metadata": {}, "outputs": [], "source": [ - "from scipy.optimize import fsolve\n", - "xzero = fsolve(f, 1)\n", - "print('result of fsolve:', xzero)\n", - "print('f(x) at xzero: ', f(xzero))\n", - "print('exact value of xzero:', -np.log(0.5))" + "def plot_line(alpha):\n", + " L = 20\n", + " x = L / 2 * np.cos(np.deg2rad(alpha))\n", + " y = L / 2 * np.sin(np.deg2rad(alpha))\n", + " plt.plot([-x, x], [-y, y])\n", + " plt.axis('scaled')\n", + " plt.xlim(-20, 20)\n", + " plt.ylim(-20, 20)\n", + " \n", + "plot_line(45)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "What now if you want to find the value of $x$ for which $f(x)=0.3$ (I know, it is $-\\ln(0.2)$). We could, of course, create a new function $f_2(x)=f(x)-0.3$ and then try to find the zero of $f_2$. But if we do that, we might as well make it more generic. Let's try to find $f(x)=a$, so we create a function $f_2=f(x)-a$" + "The `interact` function of `ipywidgets` can now be used to interact with the `plot_line` function we just defined. The `interact` function takes as input arguments the name of the function to interact with, and then the minimum value, maximum value, and step of the input arguments. When you execute the code below, a slider will appear and you can move the slider to change the value of the angle $\\alpha$ (and again, it is a bit slow, so don't move it around too quickly). " ] }, { @@ -631,15 +671,15 @@ "metadata": {}, "outputs": [], "source": [ - "def f2(x, a=0):\n", - " return f(x) - a" + "from ipywidgets import interact\n", + "interact(plot_line, alpha=(-180, 180, 5)); " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "When we use `fsolve` to find the zero of function `f2`, we need to pass it an additional argument: the value of `a`. This can be done using the keyword argument `args`, which is a tuple of additional arguments passed to the function for which `fsolve` tries to find the root. The keyword `args` can be multiple values, as long as they are separated by commas, but for our case the function `f2` only takes one additional argument." + "The `plot_line` function can be modified to also take the color of the line as an input argument. " ] }, { @@ -648,30 +688,23 @@ "metadata": {}, "outputs": [], "source": [ - "xroot = fsolve(f2, 1, args=(0.3))\n", - "print('fsolve result:', xroot)\n", - "print('f(xroot): ', f(xroot))\n", - "print('exact value: ', -np.log(0.2))" + "def plot_line(alpha, color='g'):\n", + " L = 20\n", + " x = L / 2 * np.cos(np.deg2rad(alpha))\n", + " y = L / 2 * np.sin(np.deg2rad(alpha))\n", + " plt.plot([-x, x], [-y, y], color)\n", + " plt.axis('scaled')\n", + " plt.xlim(-20, 20)\n", + " plt.ylim(-20, 20)\n", + " \n", + "plot_line(45)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Exercise 6\n", - "The cumulative density distribution $F(x)$ of the Normal distribution is given by\n", - "\n", - "$F(x)=\\frac{1}{2}\\left[ 1 + \\text{erf}\\left(\\frac{x-\\mu}{\\sqrt{2\\sigma^2}}\\right)\\right] $\n", - "\n", - "where $\\mu$ is the mean, $\\sigma$ is the standard deviation, and erf is the error function. \n", - "Recall the definition of a cumulative density distribution: When a random variable has a Normal distribution with mean $\\mu$ and standard deviation $\\sigma$, $F(x)$ is the probability that the random variable is less than $x$. Write a Python function for $F(x)$. The fist input argument should be $x$, followed by keyword arguments for $\\mu$ and $\\sigma$. The error function can be imported as\n", - "\n", - "`from scipy.special import erf`\n", - "\n", - "Test your function, for example by making sure that when $x=\\mu$, $F$ should return 0.5, and when $x=\\mu+1.96\\sigma$, $F$ should return 0.975 (remember that from your statistics class?).\n", - "\n", - "Next, find the value of $x$ for which $F(x)=p$, where $p$ is a probablity of interest (so it is between 0 and 1).\n", - "Check you answer for $\\mu=3$, $\\sigma=2$, and find $x$ for $p=0.1$ and $p=0.9$. Substitute the roots you determine with `fsolve` back into $F(x)$ to make sure your code works properly." + "The `interact` function can be used to both change the value of $\\alpha$ and the color of the line. Provide all the possible colors as a list, which will appear as a dropdown box when executing the code. " ] }, { @@ -679,34 +712,23 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, "source": [ - "Answer to Exercise 6" + "from ipywidgets import interact\n", + "interact(plot_line, alpha=(-180, 180, 5), color=['orange', 'brown', 'fuchsia']); " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Exercise 7. Numerical integration" + "### Exercise 7. First wiget" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Numerical integration of a function is a common engineering task. \n", - "The `scipy` package has a specific subpackage called `integrate` with a number of numerical integration functions. We will use the `quad` function. Use the `quad` function to integrate the function $f(x)=\\text{e}^{-x}$ from 1 till 5. Check that you did it right by doing the integration by hand (which is easy for this function). \n", - "\n", - "Next, compute the following integral:\n", - "\n", - "$$\\int_1^5 \\frac{\\text{e}^{-x}}{x}\\text{d}x$$ \n", - "\n", - "This integral is more difficult to do analytically. Perform the integration numerically with the `quad` function and check your answer, for example, at the [wolframalpha website](https://www.wolframalpha.com) where you can simply type: `integrate exp(-x)/x from 1 to 5`." + "Write a function that plots $a\\cos(x)$ and $b\\sin(x)$ on the same graph for $x$ going from $0$ to $4\\pi$. Set the limits of the vertical axis from -5 to +5. Input arguments of the function are the amplitudes $a$ and $b$, the color of the cosine function, and the color of the sine function (so 4 input arguments in total). Use the interact function to allow $a$ and $b$ to vary from 0 to 5, the color of the cosine function to be orange, pink or red, and the colors of the sine function to be blue, grey or black. " ] }, { @@ -720,7 +742,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Answer to Exercise 7" + "Answer to Exercise 7" ] }, { @@ -749,8 +771,8 @@ "x = np.linspace(0, 10 * np.pi, 100)\n", "y1 = test(x, 0.1) # This function can be called with an array\n", "y2 = test(x, 0.2)\n", - "plt.plot(x, y1,'b', label=r'$\\alpha$=0.1') # if you specify a label, it will automatically be used in the legend\n", - "plt.plot(x, y2,'r', label=r'$\\alpha$=0.2')\n", + "plt.plot(x, y1, label=r'$\\alpha$=0.1')\n", + "plt.plot(x, y2, label=r'$\\alpha$=0.2')\n", "plt.xlabel('x')\n", "plt.ylabel('f(x)')\n", "plt.legend();" @@ -901,16 +923,20 @@ "metadata": {}, "outputs": [], "source": [ - "from scipy.special import erf\n", - "def F(x, mu=0, sigma=1, p=0):\n", - " rv = 0.5 * (1.0 + erf((x - mu) / np.sqrt(2 * sigma ** 2)))\n", - " return rv - p\n", - "print('x=mu gives F(x)=', F(2, mu=2, sigma=1))\n", - "print('x=mu+1.96sig gives:', F(2+1.96, mu=2, sigma=1))\n", - "x1 = fsolve(F, 3, args=(3, 2, 0.1))\n", - "x2 = fsolve(F, 3, args=(3, 2, 0.9))\n", - "print('x1, F(x1):', x1, F(x1, mu=3, sigma=2))\n", - "print('x2, F(x2):', x2, F(x2, mu=3, sigma=2))" + "def func1(x):\n", + " return np.exp(-x)\n", + "\n", + "def func2(x):\n", + " return np.exp(-x) / x\n", + "\n", + "from scipy.integrate import quad\n", + "print('func1:')\n", + "print('numerical integration:', quad(func1, 1, 5)[0])\n", + "print('analytic integration:', -np.exp(-5) + np.exp(-1))\n", + "\n", + "print('func2:')\n", + "print('numerical integration:', quad(func2, 1, 5)[0])\n", + "print('wolframalpha result:', 0.218236)" ] }, { @@ -928,20 +954,15 @@ "metadata": {}, "outputs": [], "source": [ - "def func1(x):\n", - " return np.exp(-x)\n", - "\n", - "def func2(x):\n", - " return np.exp(-x) / x\n", - "\n", - "from scipy.integrate import quad\n", - "print('func1:')\n", - "print('numerical integration:', quad(func1, 1, 5))\n", - "print('analytic integration:', -np.exp(-5) + np.exp(-1))\n", - "\n", - "print('func2:')\n", - "print('numerical integration:', quad(func2, 1, 5))\n", - "print('wolframalpha result:', 0.218236)" + "def plot_func2(acos=1, asin=1, colorcos='C0', colorsin='C1'):\n", + " x = np.linspace(0, 4 * np.pi)\n", + " plt.plot(x, acos * np.cos(x), colorcos)\n", + " plt.plot(x, asin * np.sin(x), colorsin)\n", + " plt.ylim(-5, 5)\n", + " \n", + "interact(plot_func2, acos=(0, 5, 0.5), asin=(0, 5, 0.5), \n", + " colorcos=['orange', 'pink', 'red'], \n", + " colorsin=['blue', 'grey', 'black']);" ] }, { @@ -969,7 +990,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.8.2" }, "varInspector": { "cols": { @@ -1006,5 +1027,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/notebook4_functions/py_exploratory_comp_4_sol.ipynb b/notebook4_functions/py_exploratory_comp_4_sol.ipynb index bbb99d8..1628d05 100644 --- a/notebook4_functions/py_exploratory_comp_4_sol.ipynb +++ b/notebook4_functions/py_exploratory_comp_4_sol.ipynb @@ -33,13 +33,13 @@ "\n", "`import numpy as np`\n", "\n", - "all functions in `numpy` can be called as `np.function()`. \n", + "after which all functions in `numpy` can be called as `np.function()`. \n", "\n", "Packages can also have subpackages. For example, the `numpy` package has a subpackage called `random`, which has a bunch of functions to deal with random variables. If the `numpy` package is imported with `import numpy as np`, functions in the `random` subpackage can be called as `np.random.function()`. \n", "\n", - "If you only need one specific function, you don't have to import the entire package. For example, if you only want the cosine function of the numpy package, you may import it as `from numpy import cos`, after which you can simply call the cosine function as `cos()`. You can even rename functions when you import them. For example, after `from numpy import cos as newname`, you can call the function `newname()` to compute the cosine (I know, pretty silly, but this is can become handy). \n", + "If you only need one specific function, you don't have to import the entire package. For example, if you only want the cosine function of the numpy package, you may import it as `from numpy import cos`, after which you can simply call the cosine function as `cos()`. You can even rename functions when you import them. For example, after `from numpy import cos as newname`, you can call the function `newname()` to compute the cosine (I know, pretty silly, but this can become handy). \n", "\n", - "In the previous Notebooks we always imported `numpy` and called it `np` and we imported the plotting part of `matplotlib` and called it `plt`. Both are standard names in the Python community. The statement we added before importing `matplotlib` is `%matplotlib inline`. This latter command is an IPython command and not a Python command. It will only work in IPython and is called a magic command. All magic commands are preceded with a `%`. The statement `%matplotlib inline` puts all figures in the Notebook rather than in a separate window. \n", + "In the previous Notebooks we always imported `numpy` and called it `np` and we imported the `matplotlib.pyplot` and called it `plt`. Both are standard names in the Python community. The statement we added before importing `matplotlib` is `%matplotlib inline`. This latter command is an IPython command and not a Python command. It will only work in IPython and is called a magic command. All magic commands are preceded with a `%`. The statement `%matplotlib inline` puts all figures in the Notebook rather than in a separate window. \n", "\n", "Enough about packages for now. Let's start the way we always start." ] @@ -61,7 +61,7 @@ "source": [ "### Functions\n", "Functions are an essential part of a programming language.\n", - "You already used many functions like `plot`, `loadtxt`, or `linspace`.\n", + "You already used many functions like `plot`, `loadtxt`, and `linspace`.\n", "But you can also define your own functions.\n", "To define a new function, use the `def` command. After `def` follows the name of the function and then between parentheses the arguments of the function and finally a colon. After the colon you indent until you are done with the function. The last line of the function should be `return` followed by what you want to return. For example, consider the following function of $x$:\n", "\n", @@ -92,6 +92,7 @@ " else:\n", " f = np.exp(-x)\n", " return f\n", + "\n", "print(func(3))" ] }, @@ -127,11 +128,11 @@ "\n", "`func(` and then hit [shift-tab]\n", "\n", - "and wait a split second, the input arguments of the function pop-up in a little window, just like for other functions we already used. You can also provide additional documentation of your function. Put the documentation at the top of the indented block and put it between triple double quotes (`\"\"\"`). Run the code below to define the function `func` with the additional documentation, then in the code cell below that type \n", + "and wait a split second, the input arguments of the function pop-up in a little window, just like for other functions we already used. You can also provide additional documentation of your function. Put the documentation at the top of the indented block and put it between triple double quotes (`\"\"\"`). Run the code below to define the function `func` with the additional documentation, then in the code cell below type \n", "\n", "`func(` \n", "\n", - "and hit [shift][tab] to see the additional documentation. Warning: don't leave a code cell with just `func(` or `func()` as you will get an error on [Kernel][Restart & Run All]." + "and hit [shift][tab] to see the additional documentation. Warning: don't leave a code cell with just `func(` or `func()` as you will get an error on [Kernel][Restart & Run All Cells]." ] }, { @@ -161,7 +162,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The names of the arguments of a function are the names used inside the function. They have no relationship to the names used outside the function. When using a variable as the argument of a function, only the *value* gets passed to the function. In the example below, the *value* of `y` is passed as the first argument ot the function `func`. Inside the function, this value is used for the variable `x`." + "The names of the arguments of a function are the names used inside the function. They have no relationship to the names used outside the function. When using a variable as the argument of a function, only the *value* gets passed to the function. In the example below, the *value* of `y` is passed as the first argument of the function `func`. Inside the function, this value is used for the variable `x`." ] }, { @@ -191,7 +192,7 @@ "\n", "$f(x)=e^{-\\alpha x}\\cos(x)$\n", "\n", - "The function should take `x` and `alpha` as input arguments and return the function value. Give your function a unique name (if you also call it `func` it will overwrite the `func` function that we defined above). Make a plot of `f` vs. `x` for `x` going from 0 to $10\\pi$ using two different values of `alpha`: 0.1 and 0.2. Add a legend and label the axes." + "The function should take `x` and `alpha` as input arguments and return the function value. Give your function a unique name (if you also call it `func` it will overwrite the `func` function that we defined above). Make a plot of $f(x)$ vs. $x$ for $x$ going from 0 to $10\\pi$ using two different values of $\\alpha$: 0.1 and 0.2. Add a legend and label the axes." ] }, { @@ -219,9 +220,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -238,19 +237,27 @@ "source": [ "def testfunc(x, A=1, theta=0):\n", " return A * np.cos(np.pi * x + theta)\n", + "\n", "print(testfunc(1)) # Uses default A=1, theta=0: cos(pi)\n", "print(testfunc(1, A=2)) # Now A=2, and theta is still 0: 2*cos(pi)\n", - "print(testfunc(1, A=2, theta=np.pi / 4)) # Now A=2, theta=pi/4: 2*cos(5pi/4) \n", + "print(testfunc(1, A=2, theta=np.pi / 4)) # Now A=2, theta=pi/4: 2*cos(5pi/4)\n", "print(testfunc(1, theta=np.pi / 4, A=2)) # Same as above: 2*cos(5pi/4)\n", "print(testfunc(1, theta=np.pi / 4)) # Now theta=pi/4, and A is still 1: cos(5pi/4)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the proper style was applied, as defined in Notebook 1: there are spaces around mathematical symbols, but not around the equal sign of the keyword argument. " + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Local variables\n", - "Variables declared inside a function can only be used inside that function. The outside of a function doesn't know about the variables used inside the function, except for the variables that are returned by the function. In the code below, remove the `#` before `print(a)` and you will get an error message, as `a` is a local variable inside the function `localtest` (then put the `#` back, else you get an error when running [Kernel][Restart & Run All])." + "Variables declared inside a function can only be used inside that function. The outside of a function doesn't know about the variables used inside the function, except for the variables that are returned by the function. In the code below, remove the `#` before `print(a)` and you will get an error message, as `a` is a local variable inside the function `localtest` (then put the `#` back, else you get an error when running [Kernel][Restart & Run All Cells])." ] }, { @@ -305,14 +312,17 @@ " a = 3\n", " b = 5\n", " return a * x + b\n", + "\n", "print(test1(4))\n", "\n", "# This function also works, but it is sloppy coding\n", "# since variable a is defined outside the function\n", "a = 3\n", + "\n", "def test2(x):\n", " b = 5\n", " return a * x + b\n", + "\n", "print(test2(4)) " ] }, @@ -332,16 +342,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "Inside the functino test3, var1 equals: 4\n", + "Inside the function test3, var1 equals: 4\n", "Value of var1 outside test3: 8\n" ] } ], "source": [ "var1 = 8\n", + "\n", "def test3():\n", " var1 = 4\n", - " print('Inside the functino test3, var1 equals:', var1)\n", + " print('Inside the function test3, var1 equals:', var1)\n", + " \n", "test3()\n", "print('Value of var1 outside test3:', var1)" ] @@ -364,13 +376,13 @@ "The stream function is a function that is constant along stream lines. \n", "The stream function $\\psi$ is a function of polar coordinates $r$ and $\\theta$. The stream function is constant and equal to zero on the cylinder and doesn't really exist inside the cylinder, so let's make it zero there, like it is on the cylinder.\n", "\n", - "$\\begin{split}\n", + "$$\\begin{split}\n", "\\psi &= 0 \\qquad r\\le R \\\\\n", "\\psi &= U(r-R^2/r)\\sin(\\theta) \\qquad r\\ge R\n", - "\\end{split}$\n", + "\\end{split}$$\n", "\n", "where $U$ is the flow in the $x$-direction, $r$ is the radial distance from the center of the cylinder, $\\theta$ is the angle, and $R$ is the radius of the cylinder. You may recall it is not always easy to compute the correct angle when given a value of $x$ and $y$, as the regular arctan function returns a value between $-\\pi/2$ and $+\\pi/2$ (radians), while if $x=-2$ and $y=2$, the angle should be $3\\pi/4$.\n", - "`numpy` has a very cool function to compute the correct angle between $-\\pi$ and $+\\pi$ given the $x$ and $y$ coordinates. The function is `arctan2(y,x)`. Note that the function takes as its *first* argument `y` and as its *second* argument `x`. \n", + "`numpy` has a very cool function to compute the correct angle between $-\\pi$ and $+\\pi$ given the $x$ and $y$ coordinates. The function is `arctan2(y, x)`. Note that the function takes as its *first* argument `y` and as its *second* argument `x`. \n", "\n", "Write a function that computes the stream function for flow around a cylinder. The function should take two arguments, `x` and `y`, and two keyword arguments, `U` and `R`, and should return the stream function value. If you write the function correctly, it should give `psi(2, 4, U=2, R=1.5) = 7.1`, and `psi(0.5, 0, U=2, R=1.5) = 0` (inside the cylinder)." ] @@ -409,6 +421,7 @@ " else:\n", " f = np.exp(-x)\n", " return f\n", + "\n", "x = np.linspace(-6, 6, 100)\n", "#y = func(x) # Run this line after removing the # to see the error that occurs. Then put the # back" ] @@ -425,7 +438,7 @@ "\n", "`The truth value of an array with more than one element is ambiguous` \n", "\n", - "For some values of `x` the `if` statement may be `True`, for others it may be `False`. A simple way around this problem is to vectorize the function. That means we create a new function, let's call it `funcvec`, that is a vectorized form of `func` and can be called with an array as an argument (this is by far the easiest but not necessarily the computationally fastest way to make sure a function can be called with an array as an argument)" + "For some values of `x` the `if` statement may be `True`, for others it may be `False`. A simple way around this problem is to vectorize the function. That means we create a new function, let's call it `funcvec`, that is a vectorized form of `func` and can be called with an array as an argument. This is by far the easiest but not necessarily the computationally fastest way to make sure a function can be called with an array as an argument, and, unfortunately, it won't work for all situations. " ] }, { @@ -435,12 +448,14 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -455,7 +470,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Back now to the problem of flow around a clinder. Contours of the stream function represent stream lines around the cylinder. To make a contour plot, the function to be contoured needs to be evaluated on a grid of points. The grid of points and an array with the values of the stream function at these points can be passed to a contouring routine to create a contour plot. To create a grid of points, use the function `meshgrid` which takes as input a range of `x` values and a range of `y` values, and returns a grid of `x` values and a grid of `y` values. For example, to have 5 points in the $x$-direction from -1 to +1, and 3 points in y-direction from 0 to 10:" + "Back now to the problem of flow around a clinder. Contours of the stream function represent stream lines around the cylinder. To make a contour plot, the function to be contoured needs to be evaluated on a grid of points. The grid of points and an array with the values of the stream function at these points can be passed to a contouring routine to create a contour plot. To create a grid of points, use the function `meshgrid` which takes as input an array of `x` values and an array of `y` values, and returns a grid of `x` values and a grid of `y` values. For example, to have 5 points in the $x$-direction from -1 to +1, and 3 points in y-direction from 0 to 10:" ] }, { @@ -479,7 +494,7 @@ } ], "source": [ - "x,y = np.meshgrid( np.linspace(-1,1,5), np.linspace(0,10,3) ) \n", + "x,y = np.meshgrid(np.linspace(-1, 1, 5), np.linspace(0, 10, 3)) \n", "print('x values')\n", "print(x)\n", "print('y values')\n", @@ -491,7 +506,7 @@ "metadata": {}, "source": [ "### Exercise 3, Contour plot for flow around a cylinder\n", - "Evaluate the function for the stream function around a cylinder with radius 1.5 on a grid of 100 by 100 points, where `x` varies from -4 to +4, and `y` varies from -3 to 3; use $U=1$. Evaluate the stream function on the entire grid (you need to create a vectorized version of the function you wrote to compute the stream function). Then use the `np.contour` function to create a contour plot (find out how by reading the help of the `contour` function or go to [this demo](http://matplotlib.org/examples/pylab_examples/contour_demo.html)) of the `matplotlib` gallery. You need to use the command `plt.axis('equal')`, so that the scales along the axes are equal and the circle looks like a circle rather than an ellipse. Finally, you may want to add a nice circular patch using the `fill` command and specifying a bunch of $x$ and $y$ values around the circumference of the cylinder." + "Evaluate the function for the stream function around a cylinder with radius 1.5 on a grid of 100 by 100 points, where `x` varies from -4 to +4, and `y` varies from -3 to 3; use $U=1$. Evaluate the stream function on the entire grid (you need to create a vectorized version of the function you wrote to compute the stream function). Then use the `np.contour` function to create a contour plot (find out how by reading the help of the `contour` function or go to [this demo](http://matplotlib.org/examples/pylab_examples/contour_demo.html)) of the `matplotlib` gallery). You need to use the command `plt.axis('equal')`, so that the scales along the axes are equal and the circle looks like a circle rather than an ellipse. Finally, you may want to add a nice circular patch using the `fill` command and specifying a bunch of $x$ and $y$ values around the circumference of the cylinder." ] }, { @@ -540,10 +555,12 @@ "a, b = 4, 3\n", "print('a:', a)\n", "print('b:', b)\n", + "\n", "a, b, c = 27, np.arange(4), 'hello'\n", "print('a:', a)\n", "print('b:', b)\n", "print('c:', c)\n", + "\n", "d, e, f = np.arange(0, 11, 5)\n", "print('d:', d)\n", "print('e:', e)\n", @@ -554,7 +571,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Similarly, a function may return one value or one array. Of a function may return multiple values, multiple arrays, or whatever the programmer decides to return (including nothing, of course). When multiple *things* are returned, they are returned as a tuple. They can be stored as a tuple, or, if the user knows how many *things* are returned, they can be stored in individual variables right away, as in the example above." + "Similarly, a function may return one value or one array. Or a function may return multiple values, multiple arrays, or whatever the programmer decides to return (including nothing, of course). When multiple *things* are returned, they are returned as a tuple. They can be stored as a tuple, or, if the user knows how many *things* are returned, they can be stored in individual variables right away, as in the example below." ] }, { @@ -579,9 +596,11 @@ " dump = 4 * np.ones(5)\n", " dump[0] = 100\n", " return 33, dump, 'this works great!'\n", + "\n", "test = newfunc()\n", "print(type(test))\n", "print(test[1]) \n", + "\n", "a, b, c = newfunc()\n", "print('a:', a)\n", "print('b:', b)\n", @@ -706,151 +725,192 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Finding the zero of a function\n", - "Finding the zero of a function is a common task in exploratory computing. The value where the function equals zero is also called the *root* and finding the zero is referred to as *root finding*. There exist a number of methods to find the zero of a function varying from robust but slow (so it always finds a zero but it takes quite a few function evaluations) to fast but not so robust (it can find the zero very fast, but it won't always find it). Here we'll use the latter one.\n", + "### Exercise 6. Numerical integration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Numerical integration of a function is a common engineering task. \n", + "The `scipy` package has a specific subpackage called `integrate` with a number of numerical integration functions. We will use the `quad` function. Use the `quad` function to integrate the function $f(x)=\\text{e}^{-x}$ from 1 till 5 (note that `quad` returns two values; read the doc string to find out what they are). Check that you did it right by doing the integration by hand (which is easy for this function). \n", + "\n", + "Next, compute the following integral:\n", + "\n", + "$$\\int_1^5 \\frac{\\text{e}^{-x}}{x}\\text{d}x$$ \n", "\n", - "Consider the function $f(x)=0.5-\\text{e}^{-x}$. The function is zero when $x=-\\ln(0.5)$, but let's pretend we don't know that and try to find it using a root finding method. First, we need to write a Python function for $f(x)$." + "This integral is more difficult to do analytically. Perform the integration numerically with the `quad` function and check your answer, for example, at the [wolframalpha website](https://www.wolframalpha.com) where you can simply type: `integrate exp(-x)/x from 1 to 5`." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "def f(x):\n", - " return 0.5 - np.exp(-x)" + "Answer to Exercise 6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We will use the method `fsolve` to find the zero of a function. `fsolve` is part of the `scipy.optimize` package. `fsolve` takes two arguments: the function for which we want to find the zero, and a starting value for the search (not surpisingly, the closer the starting value is to the root, the higher the chance that `fsolve` will find it)." + "### Interactive functions using `ipywidgets`\n", + "The package `ipywidgets` constains widgets for use in Jupyter Notebooks. We will start here by using the simplest form, which is the `interact` function. The `interact` function can be used to, you guessed it, interact with a function. The `interact` function is a bit slow when interacting with a graph, but other than that it works nicely. \n", + "\n", + "For example, let's write a function that plots a line with length $L$ that makes an angle $\\alpha$ with the horizontal. The angle $\\alpha$ is an input argument." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "result of fsolve: [0.69314718]\n", - "f(x) at xzero: [0.]\n", - "exact value of xzero: 0.6931471805599453\n" - ] + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "from scipy.optimize import fsolve\n", - "xzero = fsolve(f, 1)\n", - "print('result of fsolve:', xzero)\n", - "print('f(x) at xzero: ', f(xzero))\n", - "print('exact value of xzero:', -np.log(0.5))" + "def plot_line(alpha):\n", + " L = 20\n", + " x = L / 2 * np.cos(np.deg2rad(alpha))\n", + " y = L / 2 * np.sin(np.deg2rad(alpha))\n", + " plt.plot([-x, x], [-y, y])\n", + " plt.axis('scaled')\n", + " plt.xlim(-20, 20)\n", + " plt.ylim(-20, 20)\n", + " \n", + "plot_line(45)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "What now if you want to find the value of $x$ for which $f(x)=0.3$ (I know, it is $-\\ln(0.2)$). We could, of course, create a new function $f_2(x)=f(x)-0.3$ and then try to find the zero of $f_2$. But if we do that, we might as well make it more generic. Let's try to find $f(x)=a$, so we create a function $f_2=f(x)-a$" + "The `interact` function of `ipywidgets` can now be used to interact with the `plot_line` function we just defined. The `interact` function takes as input arguments the name of the function to interact with, and then the minimum value, maximum value, and step of the input arguments. When you execute the code below, a slider will appear and you can move the slider to change the value of the angle $\\alpha$ (and again, it is a bit slow, so don't move it around too quickly). " ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f8904008968d4fafbda32d791b99e4f3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=0, description='alpha', max=180, min=-180, step=5), Output()), _dom_clas…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "def f2(x, a=0):\n", - " return f(x) - a" + "from ipywidgets import interact\n", + "interact(plot_line, alpha=(-180, 180, 5)); " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "When we use `fsolve` to find the zero of function `f2`, we need to pass it an additional argument: the value of `a`. This can be done using the keyword argument `args`, which is a tuple of additional arguments passed to the function for which `fsolve` tries to find the root. The keyword `args` can be multiple values, as long as they are separated by commas, but for our case the function `f2` only takes one additional argument." + "The `plot_line` function can be modified to also take the color of the line as an input argument. " ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "fsolve result: [1.60943791]\n", - "f(xroot): [0.3]\n", - "exact value: 1.6094379124341003\n" - ] + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "xroot = fsolve(f2, 1, args=(0.3))\n", - "print('fsolve result:', xroot)\n", - "print('f(xroot): ', f(xroot))\n", - "print('exact value: ', -np.log(0.2))" + "def plot_line(alpha, color='g'):\n", + " L = 20\n", + " x = L / 2 * np.cos(np.deg2rad(alpha))\n", + " y = L / 2 * np.sin(np.deg2rad(alpha))\n", + " plt.plot([-x, x], [-y, y], color)\n", + " plt.axis('scaled')\n", + " plt.xlim(-20, 20)\n", + " plt.ylim(-20, 20)\n", + " \n", + "plot_line(45)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Exercise 6\n", - "The cumulative density distribution $F(x)$ of the Normal distribution is given by\n", - "\n", - "$F(x)=\\frac{1}{2}\\left[ 1 + \\text{erf}\\left(\\frac{x-\\mu}{\\sqrt{2\\sigma^2}}\\right)\\right] $\n", - "\n", - "where $\\mu$ is the mean, $\\sigma$ is the standard deviation, and erf is the error function. \n", - "Recall the definition of a cumulative density distribution: When a random variable has a Normal distribution with mean $\\mu$ and standard deviation $\\sigma$, $F(x)$ is the probability that the random variable is less than $x$. Write a Python function for $F(x)$. The fist input argument should be $x$, followed by keyword arguments for $\\mu$ and $\\sigma$. The error function can be imported as\n", - "\n", - "`from scipy.special import erf`\n", - "\n", - "Test your function, for example by making sure that when $x=\\mu$, $F$ should return 0.5, and when $x=\\mu+1.96\\sigma$, $F$ should return 0.975 (remember that from your statistics class?).\n", - "\n", - "Next, find the value of $x$ for which $F(x)=p$, where $p$ is a probablity of interest (so it is between 0 and 1).\n", - "Check you answer for $\\mu=3$, $\\sigma=2$, and find $x$ for $p=0.1$ and $p=0.9$. Substitute the roots you determine with `fsolve` back into $F(x)$ to make sure your code works properly." + "The `interact` function can be used to both change the value of $\\alpha$ and the color of the line. Provide all the possible colors as a list, which will appear as a dropdown box when executing the code. " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", + "execution_count": 20, "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "835ef54816184d2681876f20f1bd0a0d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=0, description='alpha', max=180, min=-180, step=5), Dropdown(description…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "Answer to Exercise 6" + "from ipywidgets import interact\n", + "interact(plot_line, alpha=(-180, 180, 5), color=['orange', 'brown', 'fuchsia']); " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Exercise 7. Numerical integration" + "### Exercise 7. First wiget" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Numerical integration of a function is a common engineering task. \n", - "The `scipy` package has a specific subpackage called `integrate` with a number of numerical integration functions. We will use the `quad` function. Use the `quad` function to integrate the function $f(x)=\\text{e}^{-x}$ from 1 till 5. Check that you did it right by doing the integration by hand (which is easy for this function). \n", - "\n", - "Next, compute the following integral:\n", - "\n", - "$$\\int_1^5 \\frac{\\text{e}^{-x}}{x}\\text{d}x$$ \n", - "\n", - "This integral is more difficult to do analytically. Perform the integration numerically with the `quad` function and check your answer, for example, at the [wolframalpha website](https://www.wolframalpha.com) where you can simply type: `integrate exp(-x)/x from 1 to 5`." + "Write a function that plots $a\\cos(x)$ and $b\\sin(x)$ on the same graph for $x$ going from $0$ to $4\\pi$. Set the limits of the vertical axis from -5 to +5. Input arguments of the function are the amplitudes $a$ and $b$, the color of the cosine function, and the color of the sine function (so 4 input arguments in total). Use the interact function to allow $a$ and $b$ to vary from 0 to 5, the color of the cosine function to be orange, pink or red, and the colors of the sine function to be blue, grey or black. " ] }, { @@ -864,7 +924,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Answer to Exercise 7" + "Answer to Exercise 7" ] }, { @@ -888,12 +948,14 @@ "outputs": [ { "data": { - "image/png": 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Q9rkg1lr3n3DX3/6PDvNHRlJY00Je5dBL2aIoimOpAOJI9rkgNLj/bPT+9n90mD8yClDDeRVFUQHEsYJiAfBqcu8mrIH0f3RICPcjPsRXZedVFEUFEIcKiMaGEf+2Mmw29+0j6Pjw7yl9SU8mJoSQWVDnyCIpijIEqQDiSAYjLd7hRMpqaprNepemW4dKGhACxsYNLCXJRFMIRbUtlDe4/2gzRVGcRwUQB2v375hM6L6z0bPK6kkI88PPa2Cp0CYmaB3ve1UtRFHOaiqAOJgMjNPSmbjxt/PDpQ2kDWLd9rFxQRgNgszCWgeWSlGUoUYFEAfzCIkjWtRQ4aY1kNZ2K/mVTYyKGXgA8fPyYGR0IHsKVABRlLOZCiAO5hsxjEDRQnV1pd5F6VJOeSM2CWkxg0vJPnFYMJkFtWpCoaKcxVQAcTCPYBMAbTWFOpeka4dLGwBIG0QNBCDdFEJ9q4X8qmZHFEtRlCFIBRBHc/PZ6Fml9Xh5GEgM9xvUdTo60jNVM5ainLVUAHG0jqVtG91zadvDpQ2kRgXgYezmT1+ZA+0tvV4nNSoQPy+j6gdRlLOYrkvanpECtdno3i1lOheka1mlDcxJjTh9h6UNVv8Kdr0KXgEw6ocw/jIYcT50sTyv0SAYFx885AOIlJJ3dhayp6CWicNCmDI8lOQI/yGzJLGi6EkFEEfz9KHZI4TAtnKsNonR4D4fRDVNZsob2k4fgVVfDG9dC0UZMONWMDfBwY9h7yo450648MEurzdxWAgvb8rHbLH1O6eWO7BYbTz46UFe2XIMbw8Db2w7DmjDlN9cOZMgH0+dS6go7k3X//VCiEVCiCwhRI4Q4r4u9l8thNhrf2wWQqR32pcvhNgnhNgjhMhwbcl71uobTRTVVDW511DeEx3onUZg1R6HZ+ZDxWG4/DVY/HdY9gTcmw2Tr4fNj0HW511eL90Ugtlq43BpvSuK71BNbRZueW0nr2w5xk/nJnHgTxfx1T3zeODiMWSVNnDXqj1Y3TgdjaK4A90CiBDCCDwJLAbGAFcKIcacclgeMF9KOQF4EHj2lP0LpJQTpZRTnV7gfrD4xxIrqil3s7kgWfYP+pNqIOsfhtY6uOlLGLP0xHYPb1j8MMRMgA9u1QLNKYZyR/rPXt/Ft1nlPPijcfzuB2PwMBoYERXIDbOT+OPSsXxzuJxHvsjSu5iK4tb0rIFMB3KklLlSSjOwCljW+QAp5WYpZY395VbA5OIyDogIjiNGVLtdrqissgZC/DyJCvTWNtQcg8w3YcoNEH1q7AY8feDyV0Da4J0bwHJyfq+4YB8iArzZM8RSmmzOqWT9kQp+u2Q0184cftr+a2cO56oZCTy17igf7XHP0XSK4g70DCDxQEGn14X2bd25CejcliKBL4QQO4UQK7s7SQixUgiRIYTIqKhwzRoWXqHxhIsGKmvcq2mnI4XJ9x3EGx4FYYA5d3V/Uliy1qRVtBM2/fukXUIIxsYFcajEvd5nT6SUPPJFFrHBPlzTRfDo8MDFY5meGMb97++jqtG9apKK4i70DCBd9S532egshFiAFkB+02nzbCnlZLQmsNuFEPO6OldK+ayUcqqUcmpkZORgy9wnfhEJADRXuc9kQptNcqS04UTzVc0x2PO61s9hH3rcrTHLIG0JbHkCWk8OFmPigsgub8BssTmp5I71bVY5u47X8vPzUvHxNHZ7nJeHgb8uH09Lu5UXNua5sISKMnToGUAKgWGdXpuA05byE0JMAJ4Hlkkpqzq2SymL7T/LgQ/QmsTcgmeo1tJmqXWfAFJU20KT2XqiA33jP+21j7v7doF5v9L6SnY8f9LmMbFBtFslOeWNDi6x49lskke/OEJCmB+XTe29NXREVAA/GB/Lq1uOUevG6fkVRS96BpAdQKoQIkkI4QVcAXzc+QAhRALwPnCtlPJIp+3+QojAjufAhcB+l5W8Nx1L29a7z9K2HSOwRsUGQm0B7H4dJl8HwT21GnYSPwVSztdqIeYT66GPjtUC0sEh0Iy15kApB4rrueuCVDy7m0h5ijvOG0Fjm4UXN+U7t3CKMgTpFkCklBbgDmAtcAh4W0p5QAhxqxDiVvthfwDCgf+eMlw3GtgohMgEtgOfSSnXuPgtdM/eJOTZ5D6z0TtqCCOiAmDf22Brh9m/6N9F5v8amqtg58vfb0qK8MfH08DBYvcPII9/k8OIqACWTexj0ARGxQRx0dhoXtqUR31ruxNLpyhDj67zQKSUq6WUI6WUKVLK/7Nve1pK+bT9+c1SylD7UN3vh+vaR26l2x9jO851G96BtBgC8G91n9no+ZVNRAR4aZPjDn2q1ShCEvp3kYSZkDgXNj0G7doIM6NBMCrG/TvSs0obOFRSz7Uzh/d7cufPz0ulodXCq5vznVM4RRmiht704SGi0TuK4PYKt0l3nlfVRGK4P9QVQfEuLVXJQMy7FxpLYc//vt80OjaIgyX1bvNeu/JxZhEGAUvGx/b73HHxwZw/KornN+bRYrY6oXSKMjSpAOIkrX6xRFNJbbN7NHvkVzaRFOEPhz/TNoy+eGAXSpoHcZNg+/NgDxhj4oKoa2mnuM695r10kFLySWYJs0dEENkxB6afbp6bTG1zO2sPlDq4dIoydPUpgAghooQQlwghbhdC/EQIMV0IoYJPD2wB9tnoDfrPIWhqs1De0EZihD8c/hQiRkJE6sAuJgRMuREqDkHBdkAbiQW4bT/InoJajlc3szS9l+HKPZiRFEZCmB/v7Czo/WBFOUv0GASEEAuEEGuBz9DmW8SipR35PbBPCPEnIcTglrY7QxlDTESKOipq9Z+lnV+ljZoaGdQO+RsH3nzVYdwK8AqEnS8BWmoUIdw3gHycWYyXh4GLxsUM+BoGg+CyKSY25VRRUK0W0VIU6L0GsgT4qZRympRypZTy91LKX0kplwLpwG5godNLOQR5h2tTXBoq9J8LklepBZAxDVtAWmH0IAOIdwBMuAwOfAAtNfh7e5AY7u+WHelWm+TTvSUsSIscdHbdFVNMCAHv7tT/bzoYNpskI7+a/3yVzUd7iiiq7X39F0XpSo/p3KWU9/awzwJ86PASnSECorQ0GW1Vx4GZupYl3x5Aoou/1OaoxE0e/EWn3AAZL0LmWzDzVsbEBrGvSP/a1qm25lZR0dDG0vS+D93tTlyIL3NGRPDuzkJ+cX4qBjdK1d8X9a3tPPlNDp9kFp/WXxUf4ss9C0eyYsqQSDenuIm+9oG8JoQI7vQ6UQjxtfOKNfT5hmtDZK11+ifjy6tsJiEQPHK/hVE/6HKBqH6LTdcC0c6XQUrGxAVxvLrZ7eZKfLynGH8vI+ePjnLI9S6fOoyi2hY2H63q/WA3UtHQxhXPbOX5jXmMig3iXz9OJ/MPF/Lpz+fwwMVjiA7y5pfvZPLrdzPVSDOlz/q6oNRGYJsQ4h60hIf3Ar90WqnOBPbJhMYG/Wej51c1cXFAFtS0aAHEUabcAJ/cCQXbGRObBMDhkgamJ4U57h6DYLVJ1h4sZeGY6B7zXvXHwjHRBPt68nZGQdcrO7qhgupmrn1hG6X1rTx//VQWpJ0IpsF+wYyLD+aamcN57OtsHv82hz0FtTx33VSGh/vrWGplKOhTDURK+QxwM/AR8GdgnpTyE2cWbMjzDqRJ+OPdrP+wz/zKJs4xHABPP0g4x3EX/r4z/eXvU5q4Uz9IZmEttc3tnD862mHX9PE08qOJcaw5UEqdmwzR7smxqiZWPLWZ6iYzr98846Tg0ZmH0cA9F6bx6k+mU97QxvUvbqemSeX/UnrW1yasa4EXgeuAl4HVnVcHVLpW5xlFQJu+s9HrW9upajKT1rYXTNPAw8txF/cOgHHL4eCHRHubCfP34kCx+/SDrM+qQAiYM8KxNYXlk02YLTa3nxNisdr4xao9tLZbeefWc5gyvPea4dzUSF64fhrFda2sfC2D1nbVnKV0r69zOVYAc6SUb0op7wduRQskSg+afaIJtbhmDZLu5Fc2EUQj4Y3ZMHy2428w6Rpob0Yc/IjRsYEcKmlw/D0G6LvsCtJNIYT6OzBoAhNMwQwP9+PjTP2bJ3vy33VH2VNQy/9dMp60zqtQ9mLK8FD+eXk6O/Jr+PW7e906w4Cir742Yf3Inja94/V2YIbTSnWGMPvHEk0VjW0W3cqQV9nEVMMRBBISnRBATNMgPBX2vM7omCCyyhqwWPVfG6S22UxmQS3zRzp+DRghBBdPiGPz0Uoq3GCiaFf2Ftby2NfZLJsYx8UDmED5wwlx/HpRGh9nFvPfdUedUELlTNDbRMLfCyG6rPdKKc1CiPOEEIOcVHAGC4ojUtRRXqNfs05+ZTMzDIeRRi8tgaKjCQETr4LjW5gWVIPZYvt+3omeNuZUYpMwr7sAYrNCZbaWWHLDP7W14Q98AGUHT1u6tytLJ8Zhk7B6n/tkXO7QYrZy91t7iAz05s9Lxw34OrfNT+Hi9Dj+9eUR9hW6T9Nkf9lsktK6VnYeq+ajPUV8treEoxWNWG2qZjVYvY3C2gd8IoRoBXYBFYAPkApMBL4C/urUEg5hHqHaUN66sgKICdelDHmVjfzU8wgifgp4+jrnJulXwjcPMqXmc2AWB0vqSY3ue5OJM6zPqiDY15N0U/DJOyxmbSXGDY9CXTdpSXyCYdK1MO1mCEvq8pCR0YGkRQfySWYx15+T6NjCD9J/1+VwtKKJ12+eQbDfwCdPCiH4y7Jx7Mir5q63dvPZnXMdNprNFWqazKzaUcBrW/K7zNPm62lkamIoN85O5NyRUUNuXo876C2AXCqlnC2E+DVQjpbKpB74H7BSSqmmsPbAL0Kbjd5UeQwt3rpeSWUVafIoDB9g8sS+CIqFlPMJz3kPb+NMDpbU92vNDUeTUvJddgVzRkTg0XnhqH3vwld/grrjED8Vzr0PIkdrecEMHlCVAxVZkPUZbH0KtjypDXu+8C9dBpKlE+P4x9osimpbiA9xUnDup/L6Vp7fkMfF6XHMdsDggWA/Tx65LJ1rXtjGQ58f5oGlYx1QSudqNlt4eE0Wb24/TpvFxuwR4dx2bgqmUD/iQ30xW2wcKqnnYEk9a/aX8pOXM0iJ9OfW+SmsmGxSgaQfegsgU4QQw4GrgQWn7PMFVADpQVC0Nhu9vVq/1BfBlXvwwArDHTh8tysTr0K8eyOXhh7lUIljJu0NVFZZA2X1bSf6P2xW+OL/wdYntUzCP/wnjLjg9AmVsRO0x4TLtNUkM17UAsl/Z2pp7M+586RRbD+cEMs/1mbxaWYxt8xPceE77N6/vsrGYrNx74VpDrvmnNQIbpydyEub8jlvVFT3zYJuYH9RHXeu2k1eZROXTxnGT+YkdTmAYFy8VjP97ZLRfLa3hOc25HLvu3t5b1chf18xQc2B6aPeOtGfBtYAo4CMTo+d9p9KDwIj7Qs21ekTQGqazIy17MeGAYY5ecxD2hLwCeFSwzrd54Ksz9JGvs0bGQmt9fDmFVrwmH4L3PQVpC7sfTZ+UByc93u4fTukXgjfPAjPzIWSvd8fMjzcn/RhIW4zGiunvJG3Mwq4esZwEsL9HHrt3ywaRUqkP/e/v0/XQSHdkVLywsY8LvnvJpraLLx+0wz+fumEXkefeRoN/GhSPJ/+fA4PLR/PgaJ6Lvr3d7y4Me+MGX1mtUlazFanDG7pMYBIKR+TUo4GXpRSJnd6JEkpkx1emjOM8AmiAT88dFraNq+qiRmGwzSEjgFvJ/dJePpA+hVMaNiApaGCykb9Rid9l11BWnQgMd5t8PISyPkafvAoLHkYjH1NvmAXHA8/fg2uehtaauH582Hr09+vhXLxhFgOFNdztKLRCe+kfx5ecxhfTyM/P2+Ew6/t42nk4UsnUFzXwiNrsxx+/cGQUvLQ54d58NODzB8ZxZpfzOOcfjbfCSG4YnoCX9wzj3NSIvjzpwe57X+7aHCz1Dw9qWho492dhfzxo/0s/+8mJjywltTfrSblt6sZ/Yc1Tkm/09dhvLc5/M5niWpjJD4t+kwmPF5WwySRg82Rs897Mvl6jLKd5cYNutVCWsxWduTVsGBEEKy6GsoPwVVvaR3igzHyIrhtM6ScB2t+A2/8GJqquDg9DiG0nFt62nmsmi8OlnHLvGTCAwzsId4AACAASURBVAa2aFZvpgwP47qZw3llSz47j9U45R79ZbNJfv/hfp75LpdrZw7n2WunDGreT2ywLy9cP5XfLRnNl4fK+NGTm8gpd5+5Tadqbbfy3s5CrntxOzP/9jW/eieTd3YWYjQIlk2M56dzk7n7gpHct3iUtiKpg/Xz65hjCSEWAf8BjMDzUsqHTtkv7PuXAM3ADVLKXX051100eEUR3Fbe+4FO0Ho8A2/RjmHkfNfcMHoMlrhpXFX4DV8X38PcVNe3le88VkO71cKNlf+AYxvgkme1JitH8A+HK1fB9mfhi9/DM3OJvvRFZiSF8UlmMXddkIpwRKLKAfj3V9lEBHhz09xOnf1SQnUulGRC7XHt0VAC7S1gNYPNAl7+2qgzn2AINkFoEoQmQmRal7XWexeN4suDZfzmvb18duccvD30G5VltUnufSeT93cXcev8FH6zKM0hv38hBD+dl8y4+GDueGMXy57YxGNXTnJoSpzBamyz8L+tx3h+Qy6VjWbiQ3y5ZV4yP5wQR1pMIEYXDQTQLYAIIYzAk2jriRQCO4QQH0spD3Y6bDHakOFUtImLTwEz+niuW2jxiyWm5Ygu9w4o1VYM9ExyUQ0E8Jh2IynFP2N17iaY7/imlN5sya3kfs9VRB/7FC54ANJ/7NgbCAEzboFh0+GdG+ClJfwm7Rcsz53MgeL67ztnXSmzoJYN2ZXcv3gUfs3FsHsNHP0aCndAc6dmC58QLUh4+oHRCzx8tD6i2gJoqYHmypMvHJoI0eMgbqI2hyhuEgG+ofzf8vHc+NIOnvz2KPcsHOnS99pBSskfP97P+7uL+OXCkfz8/AGusNmDWSnhfHrnHFa+upObX83gvkWjWDkvWbcvCQBtFisvb8rnv+uOUtfSztzUCG47N4VZyeG6lEvPGsh0IEdKmQsghFgFLAM6B4FlwKtS683aKoQIEULEAol9ONctWP1jiaiqo7WlGR9fx3Zs9iaybj8lxnhi/VyYHXfsJTR/fC+ji98Hrnfdfe0MBz9gpfFTrclq9l3Ou1HcJLjlO/joDiYd+if/8xrHN9sDGXfJuc67Zzde/3Izd/t8ys0H/wbf7tM2hiXDyEVapoD4yVrNwqeXxUPNTVotpTpXm1BZtg9K92vLIHcIH8GC+Ck8OjyGN9flsDgtmNEJLv5mbrXw3OfbyNy2i39M8OKykEr47iMtWLbWaY+2BmhvBkub9rB16vgXBjB62h/e4OWnBVUvf63W9f0jmFifYN49L4D/binngzXHqShI5ldLp+LjH9z//rSBsFnB0oa0tLLuQAFPf32QyroGliYGcs20ONIiANthyLNo79FmBWmz99GdMgjANA0CHDtCUs8AEg90nslVyOnpUbo6Jr6P57oFQ0g8HIfq0uPEJY1y6b2TzFkUBk8h1pU39fIjK2oRc0s/pq2hEu9A16U8by7J4pba/1AUOJ74RQ85Zt2TnvgEw+Wvws6XmfrZfYzPvBrbiMcxjF/u3PsCtLfCoU9o2vYyDxVuwiAkeM2AhQ9qI+IiBlD78/KHqNHao3Pa/5ZaKN4NRRlQtBty17GisYwVnmB98QFk5EhE9Dit2SsiFcJStJqOb2j//waWNmgsg4ZSrbmtrgjqi7SRjPXFUF+Erb6EldhY6Q0csT8AvAK0WpZP0Ikg4BehDb02dJpQKa1gbdc+cNtbtEdTFZgbtUdbA1hOTDz0Bu4G7vYGcoB/2nd4+p14ePmBh7dWqzN6afOKDB5gMGoBC7QPdWnT7t/xYW81aw+LWbun1f6zvVX7adM68QXaPIoFHQUqAT7u36+Wq9+D1Av6eVLP9AwgXf3LOnXcXHfH9OVc7QJCrARWAiQkJPSnfA7hFWZf2rY8H1wYQFqqjhNFNVkRrk+aXD/2GrzL3qNk86vEXnSPa27a3oLtretpx0jh+U8Sbxzc8rV9JgRMvZENrSMJ/+JOJr13I+xdBYv+BuFOmBtSkQU7X4HMN6ClhhbPWF5mBdf+9NcExTtu7sdJfEMgZYH2AO2DsL6I3dvW8d13X7PUWk5S4XbY/+7J53n4at94O/pYvPxBGMFg0K5hadU+vM1N0FINzTVg7qLD2sNHW0kzOJ7C0Gl8WA3B0Ylcdf40jMGxEBCtBQpPH8e9Z0ub1rzXVg+ttdrz1joO5BXwyY5sgg2tLE0NIN5PgrkZ2pu0IGC113gsrVqAsFk46aNJGLTAIoxaDcjTF7yD7MGnUwDy9KVdeLG9oIlNxxqxGr05d7SJ6alxGD29tWOMXto1DB4nfgqj9m9SGE4P3qFdZ1UYDD0DSCEwrNNrE3DqUJbujvHqw7kASCmfBZ4FmDp1qssHdvtHJQLQWnnMpfetOrIVE4DJCfmvemEaPZ09X6WQsvcVWHiX9oHhbGvuJ6D2EDdZfs3jY10/W/qc6TOYvvbP/CN2I4uPvaZNPpx1uzb5cLBNiOYmOPAh7HoVCrZqHxSjfkBp6pXMftvCzXNHOC94dEUICDYx6cJreKlqLBfuL+HjO+YwOtwDqrKhJv9EzaGx7MQHcX3RiW/hoH1YevqBf6RWe/EN035XAdEQGAuB0RBk0rYJwc5j1Vz13DbGxgXx+s0zMXo5sQPfwxsCIrVHJ2PHgs+MRm55bSeP7Gnitvkp3LkoFS8Px/0bl1Ly2b4S/rb6MEW1LSyfHM/9i0cTGeic0XWDoWcA2QGkCiGSgCLgCuCqU475GLjD3scxA6iTUpYIISr6cK5bCIvTvoVaq/Ndel/zsQzapZHgRAesf95PieH+3MsP+GfTY5C1GkY7Od/mwY9g50u853sptdEL8PNy/T9rPy8PFoyJ47fZC7jgzp/j+e2DsPFf2pyR9B9rkxijx/T9guZmyPlSS/B4ZK3Wnh8+Ahb+Wcs9FhDFv9/bi9FYxE1zHP/Nsq8eWDqWzUcrufutPXx4+2x8YtO15Y4dLLeikZtfySAuxJfnr5+GrzODRy9SIgP48PbZ/PGjAzzxbQ5fHy7n0cvSGRPXSx9TH+wrrOPBTw+yPb+aMbFB/PPydGYk65NHry90CyBSSosQ4g5gLdpQ3BellAeEELfa9z8NrEYbwpuDNoz3xp7O1eFt9CosOIgyGYKxvpvEfU7iXbabw3IYCdGuX17WaBDkRS2krOZtojf923HrsHelvhg+vhNrzER+e3wZP52o33+2ZelxfJJZzPpiIxdc8hSccwdsexoyV2lrx4enQuIc7RGaaG+vD9ba3RvLtPdSsgeObdH6HGztWtNM+pUw/lJImPX977G0rpX3dhXy42nDiApyYNNNP4X5e/GPS9O58eUd/OmTg/xt+XiH36O8oZUbXtqBQQhevnEaYQ5e32UgArw9ePTydBaNi+H+9/ex7MmNXDk9gZ+dO4KY4P7/PfYU1PL419l8fbicMH8v/nrJeH48bZjLhuMOlK7zQKSUq9GCROdtT3d6LoHb+3quOxJCUGaIwa/JhelMbDbC6g+y0TCLcb4u6gs4xZj4UJ6vXMLvCl+EY5udsxaJzQYf3ApWMzsmP0xbfiWzUvQLIPPTIokI8OLtjAIuGBMN0WNh6eNwwZ+0IJK7TkvouPOl7i9i8NRGTc36mTZpcficLkf7vLAxF5uEW+bpn4Nrwagobjs3hafWHWVGUhg/muS4RJq1zWaufX47lY1tvPHTmW6Xo2rhmGimDg/l4bVZvLHtOKt2FHDV9AQum2piTGxQj0Nra5rMrDlQyoe7i9iWV02Inye/unAk152TSJCPPv9v+0vXAHK2qPWOIbrtsOtuWH0UX2sjxf5jdBuzPsEUzB+3zeW+kA8xbvqPcwLI1ichbz1c/B++KQvCy1jNlOGhjr9PH3kaDayYYuL5DXmUN7QSFWj/JuoXpgWEWT8DqwXKD2ijjFpqtQ5aL38IjIGAGK3jvZe0+7XNZl7fdpyLJ8QyLMy1Q8O788uFI9mZX8NvP9jHuPhgRkQFDPqajW0Wrn9pB3mVTbx04zQmDgtxQEkdL9Tfi78tH8/Pzk3hiW9yeG3rMV7enE9MkA/npkWSGOFPsK8ngT4eVDa0cbSiiezyBjLya7DYJEkR/ty3eBTXzBxOgPfQ+kgeWqUdopp8TUS0fqd9eLhi7HjRTgAawyc4/17dGBcfTCveZCddzahDj2vzCvrTB9Cb4j3w9Z9h1A9h8vVseWITExNCdF+v4vKpw3hmfS7v7SzitnO7qB0YPbQ+gkH0E7yy+RjNZiu3nev6iZrd8TAaeOzKSfzgsQ2sfDWDt26ZNahO3xazlZ++ksH+ojqeunqyQ1LTO9uwMD/+fukEfnVRGuuyyvk2q5zP9pbQcEryyUAfD5IjA/jJnCSWpscxNq7nmoo7UwHEBSxBwzDW2JD1RYjQ4U6/n60wgxbpjWeMa+eddDYyOhAvDwOrfX7AKM8XYNO/Yfmzjrl4az28e6PWP3DxY9S1WjhQXMcd5zl+NnJ/pUQGMD0xjLczCrh1vuNnLTebLby8OY8LRkf1a51zV4gJ9uHpa6dw3Qvbueq5rby5ciYRA8jLVdnYxk2vZLC3sJZ/Xp7OhWNjnFBa54kM9OayqcO4bOowpJQ0m63Ut7ZT32IhzN+LiACvIRswTuWC8ZWKwb4yYUt5rkvuZynYyT6ZzLBw/T5gPI0GRscGsb0MmHYT7H0bCncO/sJSwqd3Qc0xuPRF8A9ne141Ngmz3GS0yuXThpFX2cT2vGqHX/vN7QXUNLe7Ve2js2mJYbx4wzQKapq55vltVDf1vjxwZ0crGln+381kldbzzDVTuGSSyUkldQ0hBP7eHsQG+5IWE0hkoPcZEzxABRCX8IrUhlk2lLkggFjMeJTvZ48theFh+nY4TogPZn9RPba592pj+z+7W5tcNRi7XoX978GC38LwWQBsOVqFl4eBSQnu0Ua+ZHwMgd4evLXDsSPvWsxWnl5/lJnJYbr29fRmVko4L1w/jbzKJi57ejO7jveeuVdKyUd7iljx1Gaa2iysWjlryNU8zkYqgLhAUFQiNiloq8h3/s3K9mOwmdlrSyZB5w7W8fHBNLZZyGs0wqK/allhdzw/8AsW74bPfw3J58KcEzPct+RWMSUhVPf+jw5+Xh4snRjH6v0l1LU4bj2J17bmU9HQxi8duNqgs8weEcFLN06j2WxlxVOb+cNH+7tdW2N/UR2XPb2FX6zaw7BQPz742Wy37TBXTqb6QFwgOjSIEsKw1eQ7/2bFuwDYxwhiQ/SbHwAw3qRlpt1fVEdK+nLY9Rp88xcYs0wbddQf1Xnw+mXgHwXLn/t+dntNk5lDJfW6ZYXtzhXTEnh923He3lHAT+cNfu21xjYLT607yvyRkUxLdP3cnoE4JyWCL++ZzyNrs3hlSz7v7ypigimY9GEhxAT5cKC4jsyCOo6UNxDm58VDy8dz2VT3n/ugnKBqIC4QE+xDoYzEs8EFc0GKdtNgDEEED8PTqO+fNzUqAG8PA3sL67QJcD94VMsT9Pmvv1/Rr0+aquB/K7S8Qte8d1JG0W15WrpyPed/dGW8KZhzUsJ55rtcWtsH2WwHvLQxj5rmdn55oXsFyt4EeHvwwNKxfPiz2SydGEd9azvPfZfLHz8+wFeHyokN8eFXF6bxza/O5YrpCSp4DDGqBuICPp5GygzRpDW7YC5I8W6yjCNIcIMJVx5GA2PigthXVKdtCE+Bc++Dr/8En/8GFv+99xnqbQ3w5o+1PErXfQSRJ3+Abjlaha+nkXST+zV53Hl+Klc8u5VV249zw+yBpxupa27n2Q25LBwTzQQ3fJ99kT4shHR7s1Rru5WaZjMxQT5nVIfy2UjVQFykzjuWoPYKLWOns5iboeIQu9oT3WaC2YT4YA4U1WGz2Wscc+6GWXfA9mdgzf0910RK98Ez87V5Lcufg4SZpx2yJbeKqYmhDk1m5ygzk8OZnhTG0+tzabMMvBbyzHdHaWi1uF0z3UD5eBqJDfZVweMM4H7/685QLX7xGLBBvRObscr2g7Sxoy2B4eHuEUDGxQfTZLaSW9mkbRACLvwLzPwZbHsKPr0b6ktOPslmg4yX4LnztUy0138CY5aedu3KxjaOlDUy002G73blF+enUlrfyjsZA/u7Hylr4LkNuVwyKZ7RsYNP1qcojqSasFzEGpwANWgrvoUNvlO1S8W7AdhrS+YSd6mB2Jtc9hXVnkhvIQRc9FdAaOlIdr0CSfNh+Dnaezi2WUvxkbxAq3kEdL22+tZc9+z/6OyclHCmDA/lqXVHuXzqsH7VlKw2ya/f3UuAtwe//8FoJ5ZSUQZG1UBcxCNMm4Hu1LTuxbtp9YmkjFDdh/B2SIn0x8fTwL7C+pN3CKEN7b0jA+b+SltG9dv/g4rDWvr3FS/YO8y7Dh6g9X/4exkZr8M65H0lhODO81Mpqm3hlc35/Tr35c357Cmo5YGlYwkfwIxuRXE2VQNxEb+IBCzSQGtFHoNPM9eN4j2U+adBrSDBTZqwPIwGxsYFs7ewtusDIlLhvN9pEwNb67TV7/poS24V05LCdB9t1pt5qREsHBPNw2sPMy0prE9zHAqqm3lkbRbnjYpiaXqcC0qpKP3n3v/zziBRIQGUyHDaK/Odc4O2RqjMIscjlRA/T7dKBz1xWAj7iup67kgWol/Bo6y+ldyKJrdJX9ITIQT/uHQCUYE+3PHGLuqae55c2Nhm4RerdmM0CP7yo3Gqs1lxWyqAuEhMkA8FMhJqnbS0bek+kDZ2W5IY7ibNVx1mJIXRZrGRWVDnsGuuP1IBwNzU7pu43EmInxePXzWJ0rpW7n03E9nN6LO6lnaufWEbmYV1PHzpBOJCek7trih6UgHERaKDvSmUkXg3OmkUVskeADY2x7vFHJDOpieFIcSJTm9HWJ9VQXSQN6Nj3SsjbU8mJ4Tym0Wj+OJgGfe8nUlpXetJ+6ubzFz13Fb2F9Xx5FWTWTI+VqeSKkrfqD4QF4nw96aYSPzaKqC9FTwdnGakeDcyMJZ9VX7McbMaSIifF2nRgfZZ44NPuW6x2tiQXcGicTFDrnnn5rlJVDebeWFDHp/vL+HmOclEBHixPb+aLUeraDZbefa6qSxIi+r9YoqiMxVAXMRgENT7xIEFqCuECAen4y7eTUvEeKwV0m060DubmRzOqh3HMVtsg570t6eglvpWC/NHDr0PWSEEv1k0iqumJ/Dw2iye+DYHgPgQXxakRXH1zOFunWlXUTpTAcSFWgNMUAvU5js2gLQ1QGU25bGLAdyuDwRgZnIYL2/OZ19RLVOGDy4Z4LqsCowGwZxU91+lrjvDwvx4/MpJ3LNwJJ5GgSnU/f5mitIbXfpAhBBhQogvhRDZ9p+nfeUSQgwTQnwrhDgkhDgghPhFp30PCCGKhBB77I8lrn0HA2MNTtSeVOc59sIlewHJUU+teWi4m/WBAExP0kZLbc0d/CJL646UMzkhhGBf9xlpNlBJEf4qeChDll6d6PcBX0spU4Gv7a9PZQF+KaUcDcwEbhdCdF5U+19Syon2x2rnF3nwfMPiaZC+UJHl2At3zEC3JuHjaSBqEGtRO0uYv9YPMtiO9PKGVvYX1XOu6iNQFN3pFUCWAa/Yn78C/OjUA6SUJVLKXfbnDcAhIN5lJXSC6GBfsmU81vJDjr1w8W4IiudAvQ8JYX4Y3DQl9ozkMHYeq6HdahvwNTYcqQRg/sihMXxXUc5kegWQaCllCWiBAujx66QQIhGYBGzrtPkOIcReIcSLXTWBdTp3pRAiQwiRUVFRMfiSD0JMsDfZNhOy3ME1kMIdED+F49VNJOi8jG1PZiSF02y2sr9o4PNB1h2pICLAmzEqsaCi6M5pAUQI8ZUQYn8Xj2X9vE4A8B5wl5SyI6HSU0AKMBEoAR7t7nwp5bNSyqlSyqmRkfp+a40O8iFHxuHRUgHNg+8LAKCpEmqPIeOncry62W2y8HZlepLWeb4tb2Dv3WqTbMiuYP7ISLetZSnK2cRpAURKeYGUclwXj4+AMiFELID9Z3lX1xBCeKIFj9ellO93unaZlNIqpbQBzwHTnfU+HCkmyIdsaW+FqzzimIsWZgBQE5ZOa7vNrQNIZKA3I6ICBtwPkpFfTW1zO+emqeYrRXEHejVhfQxcb39+PfDRqQcIbYbYC8AhKeU/T9nXeYruJcB+J5XToeJCfMmRJu1FhYNWJyzcAcJIrn0Elrtk4e3OrORwtudV02Lu/wJL7+8qws/LyPmjVQe6orgDvQLIQ8BCIUQ2sND+GiFEnBCiY0TVbOBa4Lwuhus+LITYJ4TYCywA7nZx+QfEx9OI2T8Os/Bx3EisogyIHktendYx7Y5DeDtbMj6WZrOVLw+V9eu81nYrn+0rYfG4WPy81PQlRXEHuvxPlFJWAed3sb0YWGJ/vhHosqFbSnmtUwvoRPFh/hTVmEhyRA3EZoOiXTD+Uo5XN2MQ2oxmdzYjKYzYYB8+3F3UrzTlaw+U0thmYcWUIT0QT1HOKCqZoouZQv3ItsVDhQP6QCqPQFs9xE/lWFUz8aG+brk2eGcGg2DZxHjWH6mgqrGtz+e9t6uI+BBfZia5f/p2RTlbuPenzRnIFOrLXnOMtjZ6a33vJ/SkKMN+0Wkcq25muBsP4e3skknxWG2Sz/aV9H4w2tofG7MrWD45Xo2+UhQ3ogKIi5lCfTlitTfdVGYP7mKFO8A7GMJHcLyqyS2TKHYlLSaQ0bFBfLC7qE/Hf7i7CJuE5ZNNTi6Zoij9oQKIi5lC/ch21Eiswp1gmkJdm5Wa5na3TKLYnUsmxbH7eC35lU09Hiel5L1dhUxOCCEpYmjUsBTlbKECiIuZQn05LqOwGjwHF0DMTVB+AOKncryqGcCt54Ccaml6PELAh3t6roXsPFbDkbJGVkxRtQ9FcTcqgLhYfIgvVozU+Awf3GTC4t0gbWCayrFq7Vu8O6cxOVVMsA/npITz/q6ibnNjmS02fv/hfmKCfFg2UY2+UhR3owKIi/l4GokM9KbII2FwNRD7DPSOEVjAkOkD6XDjOUkcr27mH2u7nhPzzPqjHC5t4C8/GkeAt5r7oSjuRgUQHZhCfckhHmqOgbl5YBcp3AGhSeAfzvGqZiICvIbch+wFY6K5duZwnv0ul68OnjyxMKe8gce/yeGHE2K5YEy0TiVUFKUnKoDowBTqx962GEBC1QBGYtmskLcBkuYCcLSikeTIAMcW0kV+94PRjI0L4pfvZFJYowVTs8XGb97bh5+3kQeWjtW5hIqidGdofWU9Q5hCfVm/PxI80VKaxKb37wLFe6CtDpLPBbQAsmhcbI+nuCsfTyP/vXoyP3xsI1c8uxUfTyP5lU1YbJJHL0snIsD9FsdSFEWjaiA6MIX6km2NQXr4QtHO/l8g9xvtZ9J8qhrbqGluZ0TU0KyBgJa/69HL0/H38iA5wp9b5ifz3HVTWT5ZdZwrijtTNRAdmEL9aMeD+sjJBOdv7P8FctdDzATwj+CofW2NlMihMwKrKxeOjeHCsTF6F0NRlH5QNRAdmEK1hIdFwZOhbH//FpcyN8Hxrd83X+WUNwKQMkT7QBRFGbpUANFBR8bcg172vo9jm/p+8rEtYGuHlAWA1v/h42lw+yy8iqKceVQA0UHHXJBdliTw8IX+NGPlfgtGb0iYBdhHYEUEqCSDiqK4nAogOjGF+nKs3gLDpvczgKyDhJngqdU4csobh3QHuqIoQ5cKIDoxhfpRWNMCiXP73g/SWK4dm3wuAC1mK0W1Lar/Q1EUXagAohNTqC/FtS1Yh8/RNvSlHyR3vfYz+VwA8iqbkBJVA1EURRcqgOjEFOpLu1VSHjSm7/0gud+Cb+j3Ew9zKuwjsKKG9hBeRVGGJhVAdGIK1RIfFtZbIWFG7wGkrREOfQIjFoLBCMDR8kYMAhLDVQBRFMX1dAkgQogwIcSXQohs+8/Qbo7LF0LsE0LsEUJk9Pd8d9YxF6SwphkS5/TeD5L5prb++fSV3286WtHIsDA/fDyNzi6uoijKafSqgdwHfC2lTAW+tr/uzgIp5UQp5dQBnu+WTKG+GA2C3IomrSMduu8Hsdlg2zMQNxlMJ34NOeWNqgNdURTd6BVAlgGv2J+/AvzIxefrztvDSGK4H1mlDVpg8A6GXa92fXDuN1rW3pm3gdDme1htkrzKJtWBriiKbvQKINFSyhIA+8+obo6TwBdCiJ1CiJWdtvf1fIQQK4UQGUKIjIqKCgcV3zHSYgLJKmsADy+Y9yvI/gKyvzz9wK1PQ0A0jDkRJ4tqWmiz2IZ8DixFUYYupwUQIcRXQoj9XTyW9eMys6WUk4HFwO1CiHn9LYeU8lkp5VQp5dTIyMj+nu5UadFBHK9uptlsgRm3QlgKrLkfLOYTB1XmQM6XMPUnWqCxO1qhcmApiqIvpwUQKeUFUspxXTw+AsqEELEA9p/l3Vyj2P6zHPgAmG7f1afz3V1aTCBSQnZZoxYcFv1Na6ra/qx2gMUMGx4Fg6cWQDpRAURRFL3p1YT1MXC9/fn1wEenHiCE8BdCBHY8By4E9vf1/KFgVEwggNYPAjDyIm2Y7vq/w/p/wH8mQOYbMOV6CDi5lS6nvJFwfy9C/b1OvayiKIpL6BVAHgIWCiGygYX21wgh4oQQq+3HRAMbhRCZwHbgMynlmp7OH2q0IbgGDncEENBqIe3N8O1fIGIkXP0eLHnktHMPltQzMjrQhaVVFEU5mS4LSkkpq4Dzu9heDCyxP88FulzrtbvzhxqjQTAyOpAjZZ0CSEQqXP8pePlD7IQuz2ttt3KopJ6b5ya7qKSKoiinUysS6iwtOpBvs04ZHTZ8Vo/nHCypp90qSTeFOLFkiqIoPVOpTHSWFhNIZWMbVY1tfT5nz/FaACYlqACiKIp+VADRWVpHR3rnZqxe7CmoJTbYh+ggH2cVS1EUpVcqgOgs7dSRWH2wp6CWicNU7UNRFH2pAKKzyABvQv08+xxAqpvMHK9uJl0FEEVRdKYCiM6EECdSmvRBZoHWxQq1TgAAB5ZJREFU/6FqIIqi6E0FEDcwKiaII6UN2Gyy12N3F9RiEDA+PtgFJVMURemeCiBuYGR0IE329c17s6eglpHRgfh7qxHYiqLoSwUQN9DXjnQpJZmqA11RFDehAogb6AggB4rrezwuv6qZupZ2FUAURXELKoC4gQBvD9JNwXxzuKzH4/YU1AAwUU0gVBTFDagA4iYWj48ls7BOWyO9G3uO1+LnZSQ1SiVRVBRFfyqAuInF42IAWLO/tMv9Uko25lSSbgrBaBCuLJqiKEqXVABxE8PD/RkbF8TqfSVd7t+WV83RiiYumRzv4pIpiqJ0TQUQN7J4XAy7jtdSUnf6cN7Xth4j2NeTiyfE6VAyRVGU06kA4kYWj48FYO0pzVjl9a2s3V/KZVNM+HoZ9SiaoijKaVQAcSMpkQGkRQey+pQAsmpHARab5OqZw3UqmaIoyulUAHEzi8fHsCO/mvKGVgAsVhtvbDvO3NQIkiL8dS6doijKCSqAuJnF42KREh5de4TMglrWHiijtL6Va1XtQ1EUN6NLQiUhRBjwFpAI5AOXSylrTjkmzX5Mh2TgD1LKfwshHgB+CnSsBftbKeVqJxfbJUZGB7BwTDRvZRTwVkYBAHHBPpw3KkrnkimKopxMr4x89wFfSykfEkLcZ3/9m84HSCmzgIkAQggjUAR80OmQf0kpH3FReV1GCMFz102lrL6V7XnVZORXMzc1Eg+jqiwqiuJe9Aogy4Bz7c9fAdZxSgA5xfnA/2/vbkKsqsM4jn9/TIqmRmqD+FZZtJOsmFxUhIWGCaYFhS7KVrZIsLZtsiiKoGgXKLkI0jDsRVplVFQE5SjmS1ZaWE0O45REDRGRPi3Of+QyzZ175zgz5/zx94Hh3vO/Lzx/nmGeOf/z8nwfET+Ob1j1MeeyKaxeMo/VS3zarpnVU1X/1s6JiF6A9NhqfWYdsHPI2CZJhyRtlzSz2QclbZTULam7v7+/2dvMzGyUxq2ASPpA0pFhftaM8nsmA/cAbzYMvwJcS7HE1Qu82OzzEbE1Iroioquzs7PETMzMbDjjtoQVEcubvSapT9LciOiVNBc4PcJX3Q0ciIjzt6ptfC5pG/DeWMRsZmbtq2oJaw+wIT3fALw7wnvXM2T5KhWdQfcCR8Y0OjMza6mqAvI8sELScWBF2kbSPEnnT8eVdGl6/a0hn39B0mFJh4A7gMcnJmwzMxtUyVlYEfEbxZlVQ8dPAasatv8CZg/zvgfHNUAzM2vJFxeYmVkpLiBmZlaKIqLqGCaMpH6g7MWIVwC/jmE4Vch9Do6/ernPIff4oZo5XBUR/7sO4qIqIBdCUndEdFUdx4XIfQ6Ov3q5zyH3+KFec/ASlpmZleICYmZmpbiAtG9r1QGMgdzn4Pirl/scco8fajQHHwMxM7NSvAdiZmaluICYmVkpLiBtkLRS0reSTqQOilmRdDLdO+ygpO6q42lH6vNyWtKRhrFZkvZKOp4em/aBqVqT+LdI+iXl4aCkVSN9R5UkLZT0kaRjko5K2pzGc8pBszlkkQdJUyR9KemrFP9Tabw2OfAxkBZSO93vKG7q2APsA9ZHxNeVBjYKkk4CXRGRzQVUkm4HBoDXImJxGnsBONPQCnlmRIzUybIyTeLfAgzk0Io53fF6bkQckDQD2A+sBR4mnxw0m8MDZJAHSQKmRcSApEnAZ8Bm4D5qkgPvgbS2FDgRET9ExD/AGxQteW0cRcQnwJkhw2soWiCTHtdOaFCj0CT+bEREb0QcSM//BI4B88krB83mkIUoDKTNSeknqFEOXEBamw/83LDdQ0a/hEkA70vaL2lj1cFcgNG2Qq6jtlox14mkq4EbgS/INAdD5gCZ5EFSh6SDFE339kZErXLgAtKahhnLbd3v1oi4iaK746NpecUmXtutmOtC0nRgN/BYRPxRdTxlDDOHbPIQEWcj4gZgAbBU0uKqY2rkAtJaD7CwYXsBcKqiWEpJfVaIiNPA2xTLcjnqG+xG2UYr5NqJiL70B+EcsI2a5yGtu+8GXo+IwaZuWeVguDnklgeAiPgd+BhYSY1y4ALS2j7gOkmLJE0G1lG05M2CpGnpACKSpgF3kW8L4NG0Qq6dnFoxpwO4rwLHIuKlhpeyyUGzOeSSB0mdki5Pz6cCy4FvqFEOfBZWG9Jpfi8DHcD2iHi24pDaJukair0OKDpQ7sghfkk7gWUUt67uA54E3gF2AVcCPwH3R0QtD1Q3iX8ZxbJJACeBRwbXsutG0m3Ap8Bh4FwafoLiGEIuOWg2h/VkkAdJ11McJO+g+Gd/V0Q8LWk2NcmBC4iZmZXiJSwzMyvFBcTMzEpxATEzs1JcQMzMrBQXEDMzK8UFxMzMSnEBMTOzUlxAzCok6eZ0U78p6a4BR+t2vyOzZnwhoVnFJD0DTAGmAj0R8VzFIZm1xQXErGLpHmv7gL+BWyLibMUhmbXFS1hm1ZsFTAdmUOyJmGXBeyBmFZO0h6LT5SKKFqybKg7JrC2XVB2A2cVM0kPAvxGxQ1IH8LmkOyPiw6pjM2vFeyBmZlaKj4GYmVkpLiBmZlaKC4iZmZXiAmJmZqW4gJiZWSkuIGZmVooLiJmZlfIf6UnEdgIYfDkAAAAASUVORK5CYII=\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -904,8 +966,8 @@ "x = np.linspace(0, 10 * np.pi, 100)\n", "y1 = test(x, 0.1) # This function can be called with an array\n", "y2 = test(x, 0.2)\n", - "plt.plot(x, y1,'b', label=r'$\\alpha$=0.1') # if you specify a label, it will automatically be used in the legend\n", - "plt.plot(x, y2,'r', label=r'$\\alpha$=0.2')\n", + "plt.plot(x, y1, label=r'$\\alpha$=0.1')\n", + "plt.plot(x, y2, label=r'$\\alpha$=0.2')\n", "plt.xlabel('x')\n", "plt.ylabel('f(x)')\n", "plt.legend();" @@ -974,12 +1036,14 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1118,24 +1184,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "x=mu gives F(x)= 0.5\n", - "x=mu+1.96sig gives: 0.9750021048517796\n", - "x1, F(x1): [0.43689687] [0.1]\n", - "x2, F(x2): [5.56310313] [0.9]\n" + "func1:\n", + "numerical integration: 0.3611414941723568\n", + "analytic integration: 0.36114149417235686\n", + "func2:\n", + "numerical integration: 0.21823563880424607\n", + "wolframalpha result: 0.218236\n" ] } ], "source": [ - "from scipy.special import erf\n", - "def F(x, mu=0, sigma=1, p=0):\n", - " rv = 0.5 * (1.0 + erf((x - mu) / np.sqrt(2 * sigma ** 2)))\n", - " return rv - p\n", - "print('x=mu gives F(x)=', F(2, mu=2, sigma=1))\n", - "print('x=mu+1.96sig gives:', F(2+1.96, mu=2, sigma=1))\n", - "x1 = fsolve(F, 3, args=(3, 2, 0.1))\n", - "x2 = fsolve(F, 3, args=(3, 2, 0.9))\n", - "print('x1, F(x1):', x1, F(x1, mu=3, sigma=2))\n", - "print('x2, F(x2):', x2, F(x2, mu=3, sigma=2))" + "def func1(x):\n", + " return np.exp(-x)\n", + "\n", + "def func2(x):\n", + " return np.exp(-x) / x\n", + "\n", + "from scipy.integrate import quad\n", + "print('func1:')\n", + "print('numerical integration:', quad(func1, 1, 5)[0])\n", + "print('analytic integration:', -np.exp(-5) + np.exp(-1))\n", + "\n", + "print('func2:')\n", + "print('numerical integration:', quad(func2, 1, 5)[0])\n", + "print('wolframalpha result:', 0.218236)" ] }, { @@ -1153,33 +1225,30 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "func1:\n", - "numerical integration: (0.3611414941723568, 4.009476019776823e-15)\n", - "analytic integration: 0.36114149417235686\n", - "func2:\n", - "numerical integration: (0.21823563880424607, 5.999095875237938e-09)\n", - "wolframalpha result: 0.218236\n" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "da78a202e79845058484b9d24780dc3e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=1.0, description='acos', max=5.0, step=0.5), FloatSlider(value=1.0, de…" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "def func1(x):\n", - " return np.exp(-x)\n", - "\n", - "def func2(x):\n", - " return np.exp(-x) / x\n", - "\n", - "from scipy.integrate import quad\n", - "print('func1:')\n", - "print('numerical integration:', quad(func1, 1, 5))\n", - "print('analytic integration:', -np.exp(-5) + np.exp(-1))\n", - "\n", - "print('func2:')\n", - "print('numerical integration:', quad(func2, 1, 5))\n", - "print('wolframalpha result:', 0.218236)" + "def plot_func2(acos=1, asin=1, colorcos='C0', colorsin='C1'):\n", + " x = np.linspace(0, 4 * np.pi)\n", + " plt.plot(x, acos * np.cos(x), colorcos)\n", + " plt.plot(x, asin * np.sin(x), colorsin)\n", + " plt.ylim(-5, 5)\n", + " \n", + "interact(plot_func2, acos=(0, 5, 0.5), asin=(0, 5, 0.5), \n", + " colorcos=['orange', 'pink', 'red'], \n", + " colorsin=['blue', 'grey', 'black']);" ] }, { @@ -1207,7 +1276,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.8.2" }, "varInspector": { "cols": { @@ -1244,5 +1313,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/notebook5_rootfinding/py_exploratory_comp_5_sol.ipynb b/notebook5_rootfinding/py_exploratory_comp_5_sol.ipynb index a846375..ecf4716 100644 --- a/notebook5_rootfinding/py_exploratory_comp_5_sol.ipynb +++ b/notebook5_rootfinding/py_exploratory_comp_5_sol.ipynb @@ -51,34 +51,6 @@ "5. Test whether $|x_1-x_2|<\\varepsilon$, where $\\varepsilon$ is a user-specified tolerance. If this is not yet the case, return to step 2." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Recall that a function may simply be passed as the argument to another function in Python. The example below contains a function called `square_me` that returns the square of any function of one variable, evaluated at the provided value of $x$. As an example, `square_me` is used with the `cos` function" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "result of square_me function: 0.427249983096\n", - "directly taking the square : 0.427249983096\n" - ] - } - ], - "source": [ - "def square_me(func, x):\n", - " return func(x) ** 2\n", - "print('result of square_me function:', square_me(np.cos, 4))\n", - "print('directly taking the square :', np.cos(4) ** 2)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -289,9 +261,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -318,27 +288,29 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -363,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -383,21 +355,21 @@ " if silent is False: print(x1, x2, f1, f2)\n", " if abs(x1 - x2) < tol:\n", " break\n", - " if abs(func(x1)) > tol:\n", + " if abs(x1 - x2) > tol:\n", " print('Maximum number of iterations reached')\n", " return x1" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "zero of function and function value: 0.6923828125 -0.000382330131828\n" + "zero of function and function value: 0.6923828125 -0.0003823301318282013\n" ] } ], @@ -408,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -437,10 +409,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "def fp(x):\n", @@ -449,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -470,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -479,10 +449,10 @@ "text": [ "starting at x=1\n", "tolerance reached in 3 iterations\n", - "xzero, f(xzero) 0.693146278462 -4.5104915336e-07\n", + "xzero, f(xzero) 0.6931462784620456 -4.5104915336047213e-07\n", "starting at x=4\n", "tolerance reached in 28 iterations\n", - "xzero, f(xzero) 0.693147180453 -5.36808375529e-11\n" + "xzero, f(xzero) 0.6931471804525837 -5.3680837552860794e-11\n" ] } ], @@ -507,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -516,11 +486,11 @@ "text": [ "tolerance reached in 4 iterations\n", "starting point is x=1\n", - "xzero, sin(xzero): 2.92356620141e-13 2.92356620141e-13\n", + "xzero, sin(xzero): 2.923566201412306e-13 2.923566201412306e-13\n", "tolerance reached in 3 iterations\n", "starting point is x=1.5\n", - "xzero, sin(xzero): -12.5663706144 -1.28649811974e-15\n", - "xzero / pi: -4.0\n" + "xzero, sin(xzero): -12.566370614359174 -1.2864981197413093e-15\n", + "xzero / pi: -4.000000000000001\n" ] } ], @@ -546,14 +516,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "x0, function value [ 2.71828183] [ -1.02140518e-14]\n" + "x0, function value [2.71828183] [-1.02140518e-14]\n" ] } ], @@ -577,27 +547,29 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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z1v7a9k5EAt45i91aO/psXzfG/AIYD1xmrbVneZ9ZwCyA5OTkH32dp1lrmbEs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\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -636,7 +608,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.0" }, "varInspector": { "cols": { diff --git a/notebook6_linear_systems/py_exploratory_comp_6_sol.ipynb b/notebook6_linear_systems/py_exploratory_comp_6_sol.ipynb index fc35c4f..f386e4a 100644 --- a/notebook6_linear_systems/py_exploratory_comp_6_sol.ipynb +++ b/notebook6_linear_systems/py_exploratory_comp_6_sol.ipynb @@ -23,9 +23,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -82,9 +80,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -125,9 +121,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -156,9 +150,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -193,9 +185,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -233,9 +223,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -383,9 +371,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -434,9 +420,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -578,9 +562,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -602,9 +584,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -635,9 +615,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -668,9 +646,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -717,9 +693,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -758,9 +732,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] }, @@ -788,9 +760,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -842,9 +812,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -876,9 +844,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -915,9 +881,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -940,9 +904,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -975,9 +937,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1028,12 +988,19 @@ "print('maximum head ', np.max(h))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Back to Exercise 5\n", + "\n", + "Answers to Exercise 6" + ] + }, { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1094,17 +1061,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Back to Exercise 5\n", + "Back to Exercise 6\n", "\n", - "Answers to Exercise 6" + "Answers to Exercise 7" ] }, { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1174,9 +1139,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1242,9 +1205,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.7.0" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebook8_pandas/py_exploratory_comp_8.ipynb b/notebook8_pandas/py_exploratory_comp_8.ipynb new file mode 100644 index 0000000..b5c883e --- /dev/null +++ b/notebook8_pandas/py_exploratory_comp_8.ipynb @@ -0,0 +1,895 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "\n", + "# Exploratory Computing with Python\n", + "*Developed by Mark Bakker*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook 8: Basics of Pandas for data analysis\n", + "In this Notebook we learn how to do basic data analysis with `pandas`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading data with Pandas\n", + "Data is often stored in CSV files (Comma Separated Values, although the values can be separated by other things than commas).\n", + "So far, we have loaded csv files with the `np.loadtxt` command.\n", + "The `loadtxt` function has some basic functionality and works just fine, but when we have more elaborate data sets we want more sophisticated functionality. \n", + "The most powerful and advanced package for data handling and analysis is called `pandas`, and is commonly imported as `pd`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use only a few functions of the `pandas` package here. Full information on `pandas` can be found on the [pandas website](http://pandas.pydata.org/). \n", + "Consider the following dataset, which is stored in the file `transport.csv`. It shows the percentage of transportation kilometers by car, bus or rail for four countries. The dataset has four columns. \n", + "\n", + "`country, car, bus, rail` \n", + "`some more explanations, yada yada yada` \n", + "`France, 86.1, 5.3, 8.6` \n", + "`Germany, 85.2, 7.1, 7.7` \n", + "`Netherlands, 86.4, 4.6, 9` \n", + "`United Kingdom, 88.2, 6.5, 5.3` \n", + "\n", + "This data file can be loaded with the `read_csv` function of the `pandas` package. The `read_csv` function has many options. We will use three of them here. The rows that need to be skipped are defined with the `skiprows` keyword (in this case row 1 with the `yada yada` text). The `skipinitialspace` keyword is set to `True` so that the column name ' car' is loaded without the initial space that is in the data file. And the `index_col` keyword is set to indicate that the names in column 0 can be used as an index to select a row." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tran = pd.read_csv('transport.csv', skiprows=[1], skipinitialspace=True, index_col=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`pandas` loads data into a `DataFrame`. A `DataFrame` is like an array, but has many additional features for data analysis. For starters, once you have loaded the data, you can print it to the screen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(tran)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When the `DataFrame` is large, you can still print it to the screen (`pandas` is smart enough not to show the entire DataFrame when it is very large), or you can simply print the first 5 lines of the `DataFrame` with the `.head()` function. \n", + "\n", + "A better option is the `display` function to display a nicely formatted `DataFrame` to the screen. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(tran)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Basic `DataFrame` manipulation\n", + "The rows and columns of a `DataFrame` may have names, as for the `tran` `DataFrame` shown above. To find out which names are used for the columns, use the `keys` function, which is accessible with the dot syntax. You can loop through the names of the columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Names of columns:')\n", + "print(tran.keys())\n", + "for key in tran.keys():\n", + " print(key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each `DataFrame` may be indexed just like an array, by specifying the row and column number using the `.iloc` syntax (which stands for *index location*), where column 0 is the column labeled `car` (the column labeled as `country` was stored as an index when reading the csv file)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(tran.iloc[0, 1]) # gives the bus data for France\n", + "print(tran.iloc[1, 0]) # gives the car data for Germany\n", + "print(tran.iloc[2, 2]) # gives the rail data for Netherlands\n", + "print(tran.iloc[3]) # all data for United Kindom\n", + "print(tran.iloc[:, 1]) # all data for bus" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, and often more explicit, values in a `DataFrame` may be selected by specifying the indices by name, using the `.loc` syntax. This is a bit more typing but it is *much* more clearly what you are doing. The equivalent of the code cell above, but using indices by name is" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(tran.loc['France', 'bus'])\n", + "print(tran.loc['Germany', 'car'])\n", + "print(tran.loc['Netherlands', 'rail'])\n", + "print(tran.loc['United Kingdom'])\n", + "print(tran.loc[:, 'bus'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are two alternative ways to access all the data in a column. First, you can simply specify the column name as an index, without having to use the `.loc` syntax. Second, the dot syntax may be used by typing `.column_name`, where `column_name` is the name of the column. Hence, the following three are equivalent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(tran.loc[:, 'car']) # all rows of 'car' column\n", + "print(tran['car']) # 'car' column \n", + "print(tran.car)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to access the data in a row, only the `.loc` notation works" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tran.loc['France']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `numpy` functions for DataFrames\n", + "`DataFrame` objects can often be treated as arrays, especially when they contain data. Most `numpy` functions work on `DataFrame` objects, but they can also be accessed with the *dot* syntax, like `dataframe_name.function()`. Simply type \n", + "\n", + "`tran.` \n", + "\n", + "in a code cell and then hit the [tab] key to see all the functions that are available (there are many). In the code cell below, we compute the maximum value of transportation by car, the country corresponding to the maximum value of transportation by car (in `pandas` this is `idxmax` rather than the `argmax` used in `numpy`), and the mean value of all transportation by car. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('maximum car travel percentage:', tran.car.max())\n", + "print('country with maximum car travel percentage:', tran.car.idxmax())\n", + "print('mean car travel percentage:', tran.car.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also find all values larger than a specified value, just like for arrays." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('all rail travel above 8 percent:')\n", + "print(tran.rail[tran.rail > 8])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The code above identified France and Netherlands as the countries with more than 8% transport by rail, but the code returned a series with the country names and the value in the rail column. If you only want the names of the countries, you need to ask for the values of the index column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(tran.index[tran.rail > 8].values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 1. Average annual rainfall by country\n", + "The file `annual_precip.csv` contains the average yearly rainfall and total land area for all the countries in the world (well, there are some missing values); the data is available on the website of the world bank. Open the data file to see what it looks like (just click on it in the Files tab on the Jupyter Dashboard). Load the data with the `read_csv` function of `pandas`, making sure that the names of the countries can be used to select a row, and perform the following tasks:\n", + "\n", + "* Print the first 5 lines of the `DataFrame` to the screen with the `.head()` function.\n", + "* Print the average annual rainfall for Panama and make sure to include the units.\n", + "* Report the total land area of the Netherlands and make sure to include the units.\n", + "* Report all countries with an average annual rainfall less than 200 mm/year\n", + "* Report all countries with an average annual rainfall more than 2500 mm/year\n", + "* Report all countries with an average annual rainfall that is within 50 mm/year of the average annual rainfall in the Netherlands" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Answers to Exercise 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding a column to a `DataFrame`\n", + "A column may be added to a `DataFrame` by simply specifying the name and values of the new column using the syntax `DataFrame['newcolumn']=something`. For example, let's add a column named `public_transport`, which is the sum of the `bus` and `rail` columns, and then find the country with the largest percentage of public transport" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tran['public_transport'] = tran.bus + tran.rail\n", + "print('Country with largest percentage public transport:', tran.public_transport.idxmax())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting DataFrames\n", + "You can plot the column or row of a DataFrame with `matplotlib` functions, as we have done in previous Notebooks, but `pandas` has also implemented its own, much more convenient, plotting functions (still based on `matplotlib` in the background, of course). The plotting capabilities of `pandas` use the *dot* syntax, like `dataframe.plot()`. All columns can be plotted simultaneously (note that the names appear on the axes and the legend is added automatically!)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tran.plot(); # plot all columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also plot one column at a time. The style of the plot may be specified with the `kind` keyword (the default is `'line'`). Check out `tran.plot?` for more options. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tran['bus'].plot(kind='bar');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sorting DataFrames\n", + "DataFrames may be sorted with the `.sort_values` function. The keyword `inplace=True` replaces the values in the DataFrame with the new sorted values (when `inplace=False` a new DataFrame is returned, which you can store in a separate variable so that you have two datasets, one sorted and one unsorted). The `sort_values` function has several keyword arguments, including `by` which is either the name of one column to sort by or a list of columns so that data is sorted by the first column in the list and when values are equal they are sorted by the next column in the list. Another keyword is `ascending`, which you can use to specify whether to sort in ascending order (`ascending=True`, which is the default), or descending order (`ascending=False`)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Data sorted by car use:')\n", + "display(tran.sort_values(by='car'))\n", + "print('Data sorted by bus use:')\n", + "display(tran.sort_values(by='bus'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Renaming columns\n", + "Sometimes (quite often, really), the names of columns in a dataset are not very convenient (long, including spaces, etc.). For the example of the transportation data, the columns have convenient names, but let's change them for demonstration purposes. You can rename columns inplace, and you can change as many columns as you want. The old and new names are specified with a Python dictionary. A dictionary is a very useful data type. It is specified between braces `{}`, and links a word in the dictionary to a value. The value can be anything. You can then use the word in the dictionary as the index, just like you would look up a word in an paper dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "firstdictionary = {'goals': 20, 'city': 'Delft'}\n", + "print(firstdictionary['goals'])\n", + "print(firstdictionary['city'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Much more on Python dictionaries can be found, for example, [here](https://www.w3schools.com/python/python_dictionaries.asp). Let's continue with renaming two of the columns of the `tran` `DataFrame`: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tran.rename(columns={'bus': 'BUS', \n", + " 'rail': 'train'}, inplace=True)\n", + "display(tran)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The index column, with the countries, is now called `'country'`, but we can rename that too, for example to `'somewhere in Europe'`, with the following syntax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tran.index.names = ['somewhere in Europe']\n", + "display(tran)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 2. Average annual rainfall by country continued\n", + "Continue with the average yearly rainfall and total land area for all the countries in the world and perform the following tasks:\n", + "\n", + "* Add a new column that stores the total average annual freshwater influx in km$^3$/year for each country. Make sure you convert your units correctly. \n", + "* Sort the data on the total average annual freshwater influx in ascending order and report the 5 countries with the largest annual freshwater influx using the `iloc` syntax. \n", + "* Make a bar graph of the 10 countries with the largest annual freshwater influx." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Answers to Exercise 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time series data\n", + "In time series data, one of the columns represents dates, sometimes including times, together referred to as datetimes. `pandas` can be used to read csv files where one of the columns includes datetime data. You need to tell `pandas` which column contains datetime values and `pandas` will try to convert that column to datetime objects. Datetime objects are very convenient as specifics of the datetime object may be assessed with the dot syntax: `.year` returns the year, `.month` returns the month, etc.\n", + "\n", + "For example, consider the following data stored in the file `timeseries1.dat`\n", + "\n", + "`date, conc` \n", + "`2014-04-01, 0.19` \n", + "`2014-04-02, 0.23` \n", + "`2014-04-03, 0.32` \n", + "`2014-04-04, 0.29` \n", + "\n", + "The file may be read with `read_csv` using the keyword `parse_dates=[0]` so that column number 0 is converted to datetimes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('timeseries1.dat', parse_dates=[0], skipinitialspace=True)\n", + "display(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The rows of the DataFrame `data` are numbered, as we have not told `pandas` what column to use as the index of the rows (we will do that later). The first column of the DataFrame `data` has datetime values. We can access, for example, the year, month, and day with the dot syntax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('datetime of row 0:', data.iloc[0, 0])\n", + "print('year of row 0:', data.iloc[0, 0].year)\n", + "print('month of row 0:', data.iloc[0, 0].month)\n", + "print('day of row 0:', data.iloc[0, 0].day)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Time series data may also contain the time in addition to the date. For example, the data of the file `timeseries2.dat`, shown below, contains the day and time. You can access the `hour` or `minutes`, but also the time of a row of the DataFrame with the `.time()` function.\n", + "\n", + "`date, conc` \n", + "`2014-04-01 12:00:00, 0.19` \n", + "`2014-04-01 13:00:00, 0.20` \n", + "`2014-04-01 14:00:00, 0.23` \n", + "`2014-04-01 15:00:00, 0.21` " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('timeseries2.dat', parse_dates=[0], skipinitialspace=True)\n", + "display(data)\n", + "print('hour of row 0:', data.iloc[0, 0].hour)\n", + "print('minute of row 0:', data.iloc[0, 0].minute)\n", + "print('time of row 0:', data.iloc[0, 0].time())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting values based on a condition\n", + "Values of a column may be changed based on a condition. For example, all values of the concentration above 0.2 may be set to 0.2 with the following syntax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data.loc[data.conc>0.2, 'conc'] = 0.2\n", + "display(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 3, Load and plot daily rainfall\n", + "Rainfall data for the Netherlands may be obtained from the website of the Royal Dutch Meteorological Society KNMI . Daily rainfall for the weather station Rotterdam in 2012 is stored in the file `rotterdam_rainfall_2012.txt`. First open the file in a text editor to see what the file looks like. At the top of the file, an explanation is given of the data in the file. Read this. Load the data file with the `read_csv` function of `pandas`. Use the keyword `skiprows` to skip all rows except for the row with the names of the columns. Use the keyword `parse_dates` to give either the name or number of the column that needs to be converted to a datetime. Don't forget the `skipinitialspace` keyword, else the names of the columns may start with a bunch of spaces. Perform the following tasks:\n", + "* Convert the rainfall data to mm/d.\n", + "* Some rainfall values in the dataset may be -1 (read the header of the file to learn why); set all rainfall values that are less than zero to zero. \n", + "* Use the `plot` function of `pandas` to create a line plot of the daily rainfall with the number of the day (so not the date) along the horizontal axis. \n", + "* Use `matplotlib` functions to add labels to the axes and set the limits along the horizontal axis from 0 to 365. \n", + "* Determine the maximum daily rainfall and the date of the maximum daily rainfall and print them to the screen." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Answers to Exercise 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 4, Compute monthly rainfall from daily rainfall\n", + "In this exercise we are going to compute the total monthly rainfall for 2012 in the City of Rotterdam using the daily rainfall measurements we loaded in the previous Exercise. Later on in this Notebook we learn convenient functions from `pandas` to do this, but here we are going to do this with a loop. Create an array of 12 zeros to store the monthly totals and loop through all the days in 2012 to compute the total rainfall for each month. The month associated with each row of the DataFrame may be obtained with the `.month` syntax, as shown above. Print the monthly totals (in mm/month) to the screen and create a bar graph of the total monthly rainfall (in mm/month) vs. the month using the `plt.bar` function of matplotlib. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Answers to Exercise 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Date times as index\n", + "The datetime of a dataset may also be used as the index of a DataFrame by specifying the column with the dates as the column to use for an index with the `index_col` keyword. Note that datetimes are given as year-month-day, so `2012-04-01` means April 1, 2012." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('timeseries1.dat', parse_dates=[0], index_col=0)\n", + "display(data)\n", + "print('data on April 1:', data.loc['2014-04-01'])\n", + "print('data on April 2:', data.loc['2014-04-02'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resampling\n", + "DataFrames have a very powerful feature called resampling. Downsampling refers to going from high frequency to low frequency. For example, going from daily data to monthly data. Upsampling refers to going from low frequency to high frequency. For example going from monthly data to daily data. For both upsampling and downsampling, you need to tell `pandas` how to perform the resampling. Here we discuss downsampling, where we compute monthly totals from daily values. First we load the daily rainfall in Rotterdam in 2012 from the file `rotterdam_rainfall_2012.txt` and specify the dates as the index (this is the column labeled as YYYYMMDD). We resample the rain to monthly totals using the `resample` function. You have to tell the `resample` function to what frequency it needs to resample. Common ones are `'A'` for yearly, `'M'` for monthly, `'W'` for weekly, `'D'` for daily, and `'H'` for hourly, but there are many other ones (see [here](http://pandas.pydata.org/pandas-docs/version/0.12.0/timeseries.html)). The keyword argument `kind` is used to tell `pandas` where to assign the computed values to. You can assign the computed value to the last day of the period, or the first day, or to the entire period (in this case the entire month). The latter is done by specifying `kind='period'`, which is what we will do here. Finally, you need to specify how to resample. This is done by adding a `numpy` function at the end of the resample statement, like\n", + "\n", + " dataframe.resample(...).npfunc()\n", + " \n", + "where `npfunc` can be any `numpy` function like `mean` for the mean (that is the default), `sum` for the total, `min`, `max`, etc. Calculating the monthly totals and making a bar graph can now be done with `pandas` as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rain = pd.read_csv('rotterdam_rainfall_2012.txt', skiprows=9,\n", + " parse_dates=['YYYYMMDD'], index_col='YYYYMMDD',\n", + " skipinitialspace=True)\n", + "rain.RH[rain.RH<0] = 0 # remove negative values\n", + "rain.RH = rain.RH * 0.1 # convert to mm/day\n", + "monthlyrain = rain.RH.resample('M', kind='period').sum()\n", + "display(monthlyrain)\n", + "monthlyrain.plot(kind='bar')\n", + "plt.ylabel('mm/month')\n", + "plt.xlabel('month');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 5, Resample weather data\n", + "The file `rotterdam_weather_2000_2010.txt` contains daily weather data at the weather station Rotterdam for the period 2000-2010 (again from the KNMI). Open the data file in an editor to see what is in it. Perform the following tasks:\n", + "* Load the data making sure the dates are used as index. \n", + "* Convert the rain and evaporation to mm/day, and the temperature to degrees Celcius. \n", + "* Set any negative rainfall (explained in the file) to zero. \n", + "* Compute total yearly rainfall, total yearly evaporation, and mean yearly temperature. \n", + "* Make a line plot of the yearly rainfall, yearly evaporation, and mean yearly temperature using the `plot` function of `pandas`. Plot the mean temperature on the secondary $y$-axis (use the help function to find out how). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Answers to Exercise 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solutions to the exercises" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Answers to Exercise 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rain = pd.read_csv('annual_precip.csv', skiprows=2, index_col=0)\n", + "#\n", + "print('First five lines of rain dataset:')\n", + "display(rain.head())\n", + "#\n", + "print()\n", + "print('Average annual rainfall in Panama is',rain.loc['Panama','precip'],'mm/year')\n", + "#\n", + "print()\n", + "print('Land area of the Netherlands is', rain.loc['Netherlands','area'], 'thousand km^2')\n", + "#\n", + "print()\n", + "print('Countries where average rainfall is below 200 mm/year')\n", + "display(rain[ rain.precip < 200 ])\n", + "#\n", + "print()\n", + "print('Countries where average rainfall is above 2500 mm/year')\n", + "display(rain[ rain.precip > 2500 ])\n", + "#\n", + "print()\n", + "print('Countries with almost the same rainfall as Netherlands')\n", + "display(rain[abs(rain.loc['Netherlands','precip'] - rain.precip) < 50])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Back to Exercise 1\n", + "\n", + "Answers to Exercise 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rain['totalq'] = rain.precip * rain.area * 1e-3\n", + "#\n", + "print('Five countries with largest annual influx:')\n", + "rain.sort_values(by='totalq', ascending=False, inplace=True)\n", + "display(rain[:5])\n", + "#\n", + "rain.totalq[:10].plot(kind='bar');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Back to Exercise 2\n", + "\n", + "Answers to Exercise 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rain = pd.read_csv('rotterdam_rainfall_2012.txt', skiprows=9,\n", + " parse_dates=['YYYYMMDD'], skipinitialspace=True)\n", + "# convert to mm/d\n", + "rain.iloc[:,2] = rain.iloc[:,2] * 0.1\n", + "# set negative values to zero\n", + "rain.loc[rain.RH < 0, 'RH'] = 0\n", + "rain.RH.plot()\n", + "plt.xlabel('day')\n", + "plt.ylabel('daily rainfall (mm/day)')\n", + "plt.xlim(0, 365)\n", + "print('Maximum daily rainfall', rain.RH.max())\n", + "print('Date of maximum daily rainfall', rain.YYYYMMDD[rain.RH.idxmax()])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Back to Exercise 3\n", + "\n", + "Answers to Exercise 4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "monthlyrain = np.zeros(12)\n", + "for i in range(len(rain)):\n", + " month = rain.iloc[i,1].month\n", + " monthlyrain[month - 1] += rain.iloc[i, 2]\n", + "print(monthlyrain)\n", + "#\n", + "plt.bar(np.arange(12), monthlyrain, width=0.8)\n", + "plt.xlabel('month')\n", + "plt.ylabel('monthly rainfall (mm/month)')\n", + "plt.xticks(np.arange(12), ['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Back to Exercise 4\n", + "\n", + "Answers to Exercise 5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weather = pd.read_csv('rotterdam_weather_2000_2010.txt', skiprows=11,\n", + " parse_dates=['YYYYMMDD'], index_col='YYYYMMDD', skipinitialspace=True)\n", + "weather.TG = 0.1 * weather.TG\n", + "weather.RH = 0.1 * weather.RH\n", + "weather.EV24 = 0.1 * weather.EV24\n", + "weather.loc[weather.RH < 0, 'RH'] = 0\n", + "yearly_rain = weather.RH.resample('A', kind='period').sum()\n", + "yearly_evap = weather.EV24.resample('A', kind='period').sum()\n", + "yearly_temp = weather.TG.resample('A', kind='period').mean()\n", + "ax1 = yearly_rain.plot()\n", + "ax1 = yearly_evap.plot()\n", + "plt.ylabel('Rain/evap (mm/year)')\n", + "ax2 = yearly_temp.plot(secondary_y=True)\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Mean yearly temperature (deg C)')\n", + "plt.legend(ax1.get_lines() + ax2.get_lines(),\n", + " ['rain', 'evap', 'temp'], loc='best');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Back to Exercise 5" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook8_pandas/py_exploratory_comp_8_sol.ipynb b/notebook8_pandas/py_exploratory_comp_8_sol.ipynb index 54cba21..0b88037 100644 --- a/notebook8_pandas/py_exploratory_comp_8_sol.ipynb +++ b/notebook8_pandas/py_exploratory_comp_8_sol.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -39,8 +39,23 @@ "Data is often stored in CSV files (Comma Separated Values, although the values can be separated by other things than commas).\n", "So far, we have loaded csv files with the `np.loadtxt` command.\n", "The `loadtxt` function has some basic functionality and works just fine, but when we have more elaborate data sets we want more sophisticated functionality. \n", - "The most powerful and advanced package for data handling and analysis is called `pandas`. We will use only a few functions of the `pandas` package here. Full information on `pandas` can be found on the [pandas website](http://pandas.pydata.org/).\n", - "\n", + "The most powerful and advanced package for data handling and analysis is called `pandas`, and is commonly imported as `pd`:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use only a few functions of the `pandas` package here. Full information on `pandas` can be found on the [pandas website](http://pandas.pydata.org/). \n", "Consider the following dataset, which is stored in the file `transport.csv`. It shows the percentage of transportation kilometers by car, bus or rail for four countries. The dataset has four columns. \n", "\n", "`country, car, bus, rail` \n", @@ -55,12 +70,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "from pandas import read_csv\n", - "tran = read_csv('transport.csv', skiprows=[1], skipinitialspace=True, index_col=0)" + "tran = pd.read_csv('transport.csv', skiprows=[1], skipinitialspace=True, index_col=0)" ] }, { @@ -72,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -98,12 +112,12 @@ "source": [ "When the `DataFrame` is large, you can still print it to the screen (`pandas` is smart enough not to show the entire DataFrame when it is very large), or you can simply print the first 5 lines of the `DataFrame` with the `.head()` function. \n", "\n", - "Another option is the `display` function of the `IPython.display` package to display a nicely formatted `DataFrame` to the screen. " + "A better option is the `display` function to display a nicely formatted `DataFrame` to the screen. " ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -181,7 +195,6 @@ } ], "source": [ - "from IPython.display import display\n", "display(tran)" ] }, @@ -190,12 +203,12 @@ "metadata": {}, "source": [ "### Basic `DataFrame` manipulation\n", - "The rows and columns of a `DataFrame` may have names (as you can see for the `tran` `DataFrame` above, when we printed it to the screen). To find out which names are used for the columns, use the `keys` function, which is accessible with the dot syntax. You can loop through the names of the columns." + "The rows and columns of a `DataFrame` may have names, as for the `tran` `DataFrame` shown above. To find out which names are used for the columns, use the `keys` function, which is accessible with the dot syntax. You can loop through the names of the columns." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -221,12 +234,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Each `DataFrame` may be indexed just like an array, by specifying the row and column number using the `.iloc` syntax (which stands for *index location*), where column 0 is the column labeled `car` (since the column labeled as `country` was stored as an index when reading the csv file; more on that later)." + "Each `DataFrame` may be indexed just like an array, by specifying the row and column number using the `.iloc` syntax (which stands for *index location*), where column 0 is the column labeled `car` (the column labeled as `country` was stored as an index when reading the csv file)." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -261,12 +274,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Alternatively, and often more powerful, values in a `DataFrame` may be selected by specifying the indices by name, using the `.loc` syntax. This is a bit more typing but *much* more explicit. The equivalent of the code cell above, but using indices by name is" + "Alternatively, and often more explicit, values in a `DataFrame` may be selected by specifying the indices by name, using the `.loc` syntax. This is a bit more typing but it is *much* more clearly what you are doing. The equivalent of the code cell above, but using indices by name is" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -301,12 +314,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "There are two alternative ways to access all the data in a column. First, you can simply specify the column name as an index, without having to use the `.loc` syntax. Second, the dot syntax may be used, like syntax `.column_name`, where `column_name` is the name of the column. Hence, the following three are equivalent" + "There are two alternative ways to access all the data in a column. First, you can simply specify the column name as an index, without having to use the `.loc` syntax. Second, the dot syntax may be used by typing `.column_name`, where `column_name` is the name of the column. Hence, the following three are equivalent" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -340,6 +353,36 @@ "print(tran.car)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to access the data in a row, only the `.loc` notation works" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "car 86.1\n", + "bus 5.3\n", + "rail 8.6\n", + "Name: France, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tran.loc['France']" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -354,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -382,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -411,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -431,7 +474,7 @@ "metadata": {}, "source": [ "### Exercise 1. Average annual rainfall by country\n", - "The file `annual_precip.csv` contains the average yearly rainfall and total land area for all the countries in the world (well, there are some missing values); the data is available on the website of the world bank. Open the data file to see what it looks like (just click on it in the Files tab on the Jupyter dashboard). Load the data with the `read_csv` function of `pandas`, making sure that the names of the countries can be used to select a row, and perform the following tasks:\n", + "The file `annual_precip.csv` contains the average yearly rainfall and total land area for all the countries in the world (well, there are some missing values); the data is available on the website of the world bank. Open the data file to see what it looks like (just click on it in the Files tab on the Jupyter Dashboard). Load the data with the `read_csv` function of `pandas`, making sure that the names of the countries can be used to select a row, and perform the following tasks:\n", "\n", "* Print the first 5 lines of the `DataFrame` to the screen with the `.head()` function.\n", "* Print the average annual rainfall for Panama and make sure to include the units.\n", @@ -465,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -486,17 +529,17 @@ "metadata": {}, "source": [ "### Plotting DataFrames\n", - "You can plot the column or row of a DataFrame with `matplotlib` functions, as we have done in previous Notebooks, but `pandas` has also implemented its own, much more convenient, plotting functions (still based on `matplotlib` in the background, of course). The plotting capabilities of `pandas` also use the *dot* syntax, like `dataframe.plot()`. All columns can be plotted simultaneously (note that the names appear on the axes and the legend is added automatically!)." + "You can plot the column or row of a DataFrame with `matplotlib` functions, as we have done in previous Notebooks, but `pandas` has also implemented its own, much more convenient, plotting functions (still based on `matplotlib` in the background, of course). The plotting capabilities of `pandas` use the *dot* syntax, like `dataframe.plot()`. All columns can be plotted simultaneously (note that the names appear on the axes and the legend is added automatically!)." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -520,12 +563,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -545,12 +588,12 @@ "metadata": {}, "source": [ "### Sorting DataFrames\n", - "DataFrames may be sorted with the `.sort_values` function. The keyword `inplace=True` replaces the values in the DataFrame with the new sorted values (when `inplace=False` a new DataFrame is returned, which you can store in a separate variable so that you have two datasets, one sorted and one unsorted). The `sort_values` funcion has several keyword arguments, including `by` which is either the name of one column to sort by or a list of columns so that data is sorted by the first column in the list and when values are equal they are sorted by the next column in the list. Another keyword is `ascending`, which you can use to specify whether to sort in ascending order (`ascending=True`, which is the default), or descending order (`ascending=False`)" + "DataFrames may be sorted with the `.sort_values` function. The keyword `inplace=True` replaces the values in the DataFrame with the new sorted values (when `inplace=False` a new DataFrame is returned, which you can store in a separate variable so that you have two datasets, one sorted and one unsorted). The `sort_values` function has several keyword arguments, including `by` which is either the name of one column to sort by or a list of columns so that data is sorted by the first column in the list and when values are equal they are sorted by the next column in the list. Another keyword is `ascending`, which you can use to specify whether to sort in ascending order (`ascending=True`, which is the default), or descending order (`ascending=False`)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -743,7 +786,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -765,12 +808,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Much more on Python dictionaries can be found, for example, [here](http://learnpythonthehardway.org/book/ex39.html). Let's continue with renaming two of the columns of the `tran` `DataFrame`:" + "Much more on Python dictionaries can be found, for example, [here](https://www.w3schools.com/python/python_dictionaries.asp). Let's continue with renaming two of the columns of the `tran` `DataFrame`: " ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -868,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1002,7 +1045,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1074,7 +1117,7 @@ } ], "source": [ - "data = read_csv('timeseries1.dat', parse_dates=[0], skipinitialspace=True)\n", + "data = pd.read_csv('timeseries1.dat', parse_dates=[0], skipinitialspace=True)\n", "display(data)" ] }, @@ -1082,12 +1125,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The rows of the DataFrame `data` are numbered, as we have not told `pandas` what column to use as the index of the rows. The first column of the DataFrame `data` has datetime values. We can access, for example, the year, month, or day with the dot syntax" + "The rows of the DataFrame `data` are numbered, as we have not told `pandas` what column to use as the index of the rows (we will do that later). The first column of the DataFrame `data` has datetime values. We can access, for example, the year, month, and day with the dot syntax" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1108,6 +1151,38 @@ "print('day of row 0:', data.iloc[0, 0].day)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can get part of the date from an entire column (so for all rows) using the `.dt` syntax" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 2\n", + "2 3\n", + "3 4\n", + "4 5\n", + "Name: date, dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.date.dt.day # day for entire date column" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1123,7 +1198,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1198,11 +1273,11 @@ } ], "source": [ - "data = read_csv('timeseries2.dat', parse_dates=[0], skipinitialspace=True)\n", - "display(data)\n", - "print('hour of row 0:', data.iloc[0, 0].hour)\n", - "print('minute of row 0:', data.iloc[0, 0].minute)\n", - "print('time of row 0:', data.iloc[0, 0].time())" + "data2 = pd.read_csv('timeseries2.dat', parse_dates=[0], skipinitialspace=True)\n", + "display(data2)\n", + "print('hour of row 0:', data2.iloc[0, 0].hour)\n", + "print('minute of row 0:', data2.iloc[0, 0].minute)\n", + "print('time of row 0:', data2.iloc[0, 0].time())" ] }, { @@ -1215,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1281,8 +1356,8 @@ } ], "source": [ - "data.loc[data.conc>0.2, 'conc'] = 0.2\n", - "display(data)" + "data2.loc[data2.conc>0.2, 'conc'] = 0.2\n", + "display(data2)" ] }, { @@ -1344,7 +1419,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1425,7 +1500,7 @@ } ], "source": [ - "data = read_csv('timeseries1.dat', parse_dates=[0], index_col=0)\n", + "data = pd.read_csv('timeseries1.dat', parse_dates=[0], index_col=0)\n", "display(data)\n", "print('data on April 1:', data.loc['2014-04-01'])\n", "print('data on April 2:', data.loc['2014-04-02'])" @@ -1445,7 +1520,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1472,7 +1547,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1484,7 +1559,7 @@ } ], "source": [ - "rain = read_csv('rotterdam_rainfall_2012.txt', skiprows=9,\n", + "rain = pd.read_csv('rotterdam_rainfall_2012.txt', skiprows=9,\n", " parse_dates=['YYYYMMDD'], index_col='YYYYMMDD',\n", " skipinitialspace=True)\n", "rain.RH[rain.RH<0] = 0 # remove negative values\n", @@ -1523,6 +1598,44 @@ "Answers to Exercise 5" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rolling mean or rolling total\n", + "The `resample` method resamples to, for example, weeks, one week at a time. The `rolling` method performs a similar computation for a moving window, where the first argument is the length of the moving window. For example, a 30 day rolling total rainfall first computes the total rainfall in the first 30 days, from day 0 till day 30, then from day 1 till day 31, from day 2 till 32, etc. The value can be assigned to the end of the rolling period, or to the center of the rolling period (by setting `center=True`). The monthly total rainfall and 30-day rolling total are compared in the figure below." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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preciparea
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Afghanistan327.0652.2
Albania1485.027.4
Algeria89.02381.7
American SamoaNaN0.2
AndorraNaN0.5
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" + ], + "text/plain": [ + " precip area\n", + "country \n", + "Afghanistan 327.0 652.2\n", + "Albania 1485.0 27.4\n", + "Algeria 89.0 2381.7\n", + "American Samoa NaN 0.2\n", + "Andorra NaN 0.5" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Average annual rainfall in Panama is 2692.0 mm/year\n", "\n", "Land area of the Netherlands is 33.7 thousand km^2\n", "\n", - "Countries where average rainfall is below 200 mm/year\n", - " precip area\n", - "country \n", - "Algeria 89.0 2381.7\n", - "Bahrain 83.0 0.8\n", - "Egypt, Arab Rep. 51.0 995.5\n", - "Jordan 111.0 88.8\n", - "Kuwait 121.0 17.8\n", - "Libya 56.0 1759.5\n", - "Mauritania 92.0 1030.7\n", - "Niger 151.0 1266.7\n", - "Oman 125.0 309.5\n", - "Qatar 74.0 11.6\n", - "Saudi Arabia 59.0 2149.7\n", - "Turkmenistan 161.0 469.9\n", - "United Arab Emirates 78.0 83.6\n", - "Yemen, Rep. 167.0 528.0\n", + "Countries where average rainfall is below 200 mm/year\n" + ] + }, + { + "data": { + "text/html": [ + "
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preciparea
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Algeria89.02381.7
Bahrain83.00.8
Egypt, Arab Rep.51.0995.5
Jordan111.088.8
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Oman125.0309.5
Qatar74.011.6
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United Arab Emirates78.083.6
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preciparea
country
Bangladesh2666.0130.2
Brunei Darussalam2722.05.3
Colombia2612.01109.5
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Fiji2592.018.3
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Papua New Guinea3142.0452.9
Sao Tome and Principe3200.01.0
Sierra Leone2526.071.6
Solomon Islands3028.028.0
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" + ], + "text/plain": [ + " precip area\n", + "country \n", + "Bangladesh 2666.0 130.2\n", + "Brunei Darussalam 2722.0 5.3\n", + "Colombia 2612.0 1109.5\n", + "Costa Rica 2926.0 51.1\n", + "Fiji 2592.0 18.3\n", + "Indonesia 2702.0 1811.6\n", + "Malaysia 2875.0 328.6\n", + "Panama 2692.0 74.3\n", + "Papua New Guinea 3142.0 452.9\n", + "Sao Tome and Principe 3200.0 1.0\n", + "Sierra Leone 2526.0 71.6\n", + "Solomon Islands 3028.0 28.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "Countries with almost the same rainfall as Netherlands\n", - " precip area\n", - "country \n", - "Burkina Faso 748.0 273.6\n", - "Lesotho 788.0 30.4\n", - "Mexico 752.0 1944.0\n", - "Netherlands 778.0 33.7\n", - "Slovak Republic 824.0 48.1\n", - "Swaziland 788.0 17.2\n" + "Countries with almost the same rainfall as Netherlands\n" ] + }, + { + "data": { + "text/html": [ + "
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preciparea
country
Burkina Faso748.0273.6
Lesotho788.030.4
Mexico752.01944.0
Netherlands778.033.7
Slovak Republic824.048.1
Swaziland788.017.2
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" + ], + "text/plain": [ + " precip area\n", + "country \n", + "Burkina Faso 748.0 273.6\n", + "Lesotho 788.0 30.4\n", + "Mexico 752.0 1944.0\n", + "Netherlands 778.0 33.7\n", + "Slovak Republic 824.0 48.1\n", + "Swaziland 788.0 17.2" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from pandas import read_csv\n", - "rain = read_csv('annual_precip.csv', skiprows=2, index_col=0)\n", + "rain = pd.read_csv('annual_precip.csv', skiprows=2, index_col=0)\n", "#\n", "print('First five lines of rain dataset:')\n", - "print(rain.head())\n", + "display(rain.head())\n", "#\n", "print()\n", "print('Average annual rainfall in Panama is',rain.loc['Panama','precip'],'mm/year')\n", @@ -1620,15 +2099,15 @@ "#\n", "print()\n", "print('Countries where average rainfall is below 200 mm/year')\n", - "print(rain[ rain.precip < 200 ])\n", + "display(rain[ rain.precip < 200 ])\n", "#\n", "print()\n", "print('Countries where average rainfall is above 2500 mm/year')\n", - "print(rain[ rain.precip > 2500 ])\n", + "display(rain[ rain.precip > 2500 ])\n", "#\n", "print()\n", "print('Countries with almost the same rainfall as Netherlands')\n", - "print(rain[abs(rain.loc['Netherlands','precip'] - rain.precip) < 50])" + "display(rain[abs(rain.loc['Netherlands','precip'] - rain.precip) < 50])" ] }, { @@ -1642,26 +2121,99 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Five countries with largest annual influx:\n", - " precip area totalq\n", - "country \n", - "Brazil 1782.0 8459.4 15074.6508\n", - "Russian Federation 460.0 16376.9 7533.3740\n", - "United States 715.0 9147.4 6540.3910\n", - "China 645.0 9327.5 6016.2375\n", - "Indonesia 2702.0 1811.6 4894.9432\n" + "Five countries with largest annual influx:\n" ] }, { "data": { - "image/png": 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\n", 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" ] @@ -1677,7 +2229,7 @@ "#\n", "print('Five countries with largest annual influx:')\n", "rain.sort_values(by='totalq', ascending=False, inplace=True)\n", - "print(rain[:5])\n", + "display(rain[:5])\n", "#\n", "rain.totalq[:10].plot(kind='bar');" ] @@ -1693,7 +2245,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1706,7 +2258,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1718,7 +2270,7 @@ } ], "source": [ - "rain = read_csv('rotterdam_rainfall_2012.txt', skiprows=9,\n", + "rain = pd.read_csv('rotterdam_rainfall_2012.txt', skiprows=9,\n", " parse_dates=['YYYYMMDD'], skipinitialspace=True)\n", "# convert to mm/d\n", "rain.iloc[:,2] = rain.iloc[:,2] * 0.1\n", @@ -1743,7 +2295,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1755,7 +2307,7 @@ }, { "data": { - "image/png": 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t4ua2uQALhCStYn16EOuBPaqqhk5GkjQ/+kyUuwi4z9CJSJLmy7hhrp+iOZV0N+BbSb4O3LjwflXtP3x6kqRZGXeK6U1Ty0KSNHfGDXP9EkCSN1TV4aPvJXkD8KWBc5MkzVCfaxBP6Gh70qQTkSTNl0ULRJK/S3IhsHuSC0YelwMXrjRwkm2SfCPJp9vt+7X3nPhekg8nucNKY0iSlm9cD+KDwFOBU9vnhcefVdVzJxD7FcAlI9tvAN5cVbsBPwUOnUAMSdIyLVogqurnVXVFVR0EbAJ+RzOq6a5J1q4kaJJdgP2Ad7fboVnb6WPtLicBT1tJDEnSyvSZSf1S4GjgR8AtbXMBe64g7luA19AMoQW4F/Czqrqp3d4E7LxIPhuADQBr166oTkmSxugzk/rvgd2r6seTCJjkKcC1VXVukscsNHfs2jlzu6o2AhsB1q9f7+xuSRpInwJxFTDJ+z48Ctg/yZOBOwJ3p+lRbJdk27YXsQtw9QRjSpK2UJ8CcRlwVpLTuPVM6uOXE7CqjgSOBGh7EK+uqucm+SjwLOBk4BDgk8s5viRpMvrMg7gSOB24A801g4XHpB0OHJbkUpprEicMEEOS1NOSPYiqej1Akrs1m/XLSQWvqrOAs9rXl+E9riVpbizZg0jykCTfoFnV9eIk5yZ58PCpSZJmqc8ppo3AYVV136q6L/Aq4F3DpiVJmrU+BeIuVXXmwkZ7Wugug2UkSZoLvUYxJfmfwPvb7b8BLh8uJUnSPOjTg3ghsAY4BfiX9vULhkxKkjR7fUYx/RR4+RRykSTNkT5rMa0HXgusG92/qlayFpMkac71uQbxAeAfaO4BccsS+0qSVok+BeK6qjp18EwkSXOlT4E4Ksm7gTO49VpMpwyWlSRp5voUiBcADwJuz63vB2GBkKRVrE+B2Kuq/uvgmUiS5kqfeRBnJ9lj8EwkSXOlTw/i0cAhSS6nuQYRmlVdHeYqSatYnwLxxMGzkCTNnT4zqX8wjUQkSfOlTw9CK7DuiNMmfswrjt1v4seUpM31uUgtSdoKTb1AJNk1yZlJLklycZJXtO3bJzk9yffa53tOOzdJ0h/M4hTTTcCrquq89j7X5yY5HXg+cEZVHZvkCOAI4PAZ5Heb5KksSZM29R5EVV1TVee1r38BXALsDBwAnNTudhLwtGnnJkn6g5leg0iyDngY8DXg3lV1DTRFBNhxdplJkmZWIJLcFfg48PdVdcMWfG5DknOSnHPdddcNl6AkbeVmUiCS3J6mOHxgZFXYHyXZqX1/J+Dars9W1caqWl9V69esWTOdhCVpKzSLUUwBTgAuqarjR946FTikfX0I8Mlp5yZJ+oNZjGJ6FPA84MIk57dtrwWOBT6S5FDgSuDAGeQmSWpNvUBU1VdoFvzr8rhp5iJJWpwzqSVJnSwQkqROFghJUicLhCSpkwVCktTJAiFJ6mSBkCR1skBIkjpZICRJnSwQkqROFghJUicLhCSpkwVCktTJAiFJ6mSBkCR1skBIkjpZICRJnSwQkqROFghJUqe5KxBJnpjkO0kuTXLErPORpK3VXBWIJNsAbweeBOwBHJRkj9lmJUlbp7kqEMA+wKVVdVlV/RY4GThgxjlJ0lZp3grEzsBVI9ub2jZJ0pSlqmadw+8lORD471X1t+3284B9quplI/tsADa0m7sD35lCajsA16+SOKvpu6y2OKvpuxhnfmMA3Leq1iy107ZTSGRLbAJ2HdneBbh6dIeq2ghsnGZSSc6pqvWrIc5q+i6rLc5q+i7Gmd8YW2LeTjH9O7BbkvsluQPwbODUGeckSVuluepBVNVNSV4K/CuwDXBiVV0847Qkaas0VwUCoKo+A3xm1nlsZlqntKYRZzV9l9UWZzV9F+PMb4ze5uoitSRpfszbNQhJ0pywQPSQ5JcDH//mJOePPNZN+PiV5P0j29smuS7JpycZZ+T4T29jPmig40/1+7QxBv0dmFacoX82I3Fel+TiJBe0v9N/PkCMXZJ8Msn3knw/yVvbwS2TjFFJjhvZfnWSoycZoz3uwt+Ai5N8M8lhSWb+93nmCQiAX1fVQ0ceV0z4+P8JPCTJndrtJwA/nHCMUQcBX6EZhTaEaX+f1WTonw1JHgE8Bdi7qvYEHs+tJ8BOIkaAU4BPVNVuwJ8CdwWOmWQc4EbgGUl2mPBxN7fwN+DBNL/PTwaOGjjmkiwQW4/PAvu1rw8CPjREkCR3BR4FHMqAf4SY0vdZTab4s9kJuL6qbgSoquur6uolPrOl9gV+U1XvaWPcDLwSeGGSO08wzk00F45fOcFjjlVV19JMBn5pWwhnxgIxH+40cnrpXwaKcTLw7CR3BPYEvjZQnKcBn6uq7wI/SbL3QHGm9X1Wk2n9bD4P7Jrku0n+b5K/HCDGg4FzRxuq6gbgSuCBE471duC5Se4x4eMuqqouo/n7vOO0YnaxQMyH0VNMTx8iQFVdAKyj+W97yGHEB9H88aZ9PmiIIFP8PqvJtH42vwT+jOa/4OuADyd5/oTDBOgagrlY+7K1hed9wMsnedweZtp7gDmcB6FBnQq8CXgMcK9JHzzJvWi6/g9JUjSTHSvJa2qY8dSDfp/VZNo/m/aUz1nAWUkuBA4B3jvBEBcDzxxtSHJ3mqV6vj/BOAveApwHvGeAY/+RJPcHbgaunUa8xdiD2LqcCPxTVV040PGfBbyvqu5bVeuqalfgcuDRA8Ub+vusJlP72STZPcluI00PBX4w4TBnAHdOcnAbcxvgOOC9VfWrCceiqn4CfITm+s2gkqwB3gn880D/WPVmgVhCkm1pRjLc5lXVpqp664AhDgI2v4byceA5QwSbwvcBpvc7MHCcaf5s7gqclORbSS6gufnX0ZMM0P7hfDpwYJLvAd8FfgO8dpJxNnMczWqrQ1i4Dnkx8AWa6zivHyhWb86kXkKSvYB3VdU+s85FszGt3wF/1zRv7EGMkeRFNMMn/3HWuWg2pvU74O+a5pE9CElSJ3sQkqROFghJUicLhCSpkwVCmqIk2yV58cj2Y4ZchVZaCQuENF3bAS9eci9pDlggpEUkWZfk20neneSiJB9I8vgk/9beg2CfJNsn+UR734Ozk+zZfvboJCcmOSvJZUkW1vE5FnhAOynqjW3bXZN8rI31gVmv4CktcC0mabwHAgfSLDz37zQzjx8N7E8za/cq4BtV9bQk+9Is6vbQ9rMPAh4L3A34TpJ3AEcAD6mqh0Jzigl4GM3qpFcD/0azJPdXpvHlpHHsQUjjXV5VF1bVLTQLxJ3RLvNwIc1qso8G3g9QVV8E7jWyLPRpVXVjVV1Ps+javReJ8fV22ZBbgPPb40ozZ4GQxhtdG+mWke1baHrgXaeDFmafjn72ZhbvsffdT5oqC4S0Ml8Gngu/P110fXv/gMX8guaUkzT3/E9FWpmjgfe0q5b+iua+B4uqqh+3F7kvorlt6mnDpygtj2sxSZI6eYpJktTJAiFJ6mSBkCR1skBIkjpZICRJnSwQkqROFghJUicLhCSp0/8HQroy4YDyhUoAAAAASUVORK5CYII=\n", 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1nYwkabzaDF67Gnha14lIksZvpltSv0jvNNHmwLVJLgHun9xeVQd1n54kaZRmOn30gZFlIUmaF2a6JfXbAEneX1VHD25L8n7g2x3nJkkasTbXFF4yRdtLh52IJGn8pi0KSf46yVXALkmuHHjcBFy1sYGTbJLkB0m+1LzfMsnXk/y4eX7KxsaQJG2YmXoKnwFeAZzfPE8+/qSqXj+E2H8LXDfwfgVwYVXtTG9G1hVDiCFJ2gDTFoWq+lVV3VxVhwFrgN/TuxtpsyQ7bEzQJNsBBwKnDDQfDJzWvD4NeOXGxJAkbbg2I5rfDhwH3AE81DQXsMdGxP0g8B56t7tO2qaqbgeoqtuTbD1NPsuB5QA77LBRtUmStJ42I5r/Dtilqu4aRsAkLwfWVtWlSfbZ0M9X1SpgFcCyZcscZS1JQ9SmKNwKDHPdhBcAByV5GfA4YIsk/wzckWRp00tYCqwdYkxJUgttisKNwEVJLuDhI5pPnkvAqjoGOAag6Sm8u6rekORE4HBgZfN83lyOL0mauzZF4Zbm8Zjm0ZWVwNlJjmziHdJhLEnSFGYtClV1PECSzXtv69fDCl5VFwEXNa/vAvYf1rElSRtu1hHNSZ6V5Af0Zku9JsmlSXbvPjVJ0qi1meZiFXBUVe1YVTsC7wI+3m1akqRxaFMUnlhV35p805zyeWJnGUmSxqbV3UdJ/idwevP+DcBN3aUkSRqXNj2FNwNLgHOBf2leH9FlUpKk8Whz99EvgHeMIBdJ0pi1mftoGfBeYGJw/6ramLmPJEnzUJtrCmcAf09vDYWHZtlXkvQI1qYorKuq8zvPRJI0dm2KwrFJTqG38M3g3EfndpaVJGks2hSFI4BdgUfz8PUULAqStMC0KQp7VtV/7TwTSdLYtRmncHGS3TrPRJI0dm16Ci8EDk9yE71rCqE3W6q3pErSAtOmKBzQeRaSpHmhzYjmn44iEUnS+LXpKWgjTKy4YOjHvHnlgUM/piRBuwvNkqRFwqIgSeob+emjJNsDnwaeRm8w3Kqq+lCSLYHP0pt472bgdc0MrWrB01SShmEcPYUHgHdV1X8B/hR4WzMOYgVwYVXtTG9KjRVjyE2SFrWRF4Wqur2qLmte3wNcB2wLHAyc1ux2GvDKUecmSYvdWK8pJJkAngN8H9imqm6HXuEAtp7mM8uTrE6yet26daNKVZIWhbEVhSSbAecAf1dVd7f9XFWtqqplVbVsyZIl3SUoSYvQWIpCkkfTKwhnDEzBfUeSpc32pcDaceQmSYvZyItCkgCfAK6rqpMHNp0PHN68Phw4b9S5SdJiN44RzS8A/hK4KsnlTdt7gZXA2UmOBG4BDhlDbpK0qI28KFTVd+nNtDqV/UeZiyTp4RzRLEnqsyhIkvosCpKkPouCJKnPoiBJ6rMoSJL6LAqSpD6LgiSpz6IgSeqzKEiS+iwKkqQ+i4Ikqc+iIEnqsyhIkvosCpKkPouCJKnPoiBJ6rMoSJL6LAqSpL55VxSSHJDk+iQ3JFkx7nwkaTGZV0UhySbAh4GXArsBhyXZbbxZSdLiMa+KArA3cENV3VhVvwPOAg4ec06StGikqsadQ1+S1wIHVNVfNe//EnhuVb19YJ/lwPLm7S7A9SNIbSvgzgUSZyF9F+PM3xjGmd9xdqyqJVNt2LTjwBsqU7Q9rGpV1Spg1WjS6UmyuqqWLYQ4C+m7GGf+xjDO/I8znfl2+mgNsP3A++2A28aUiyQtOvOtKPw7sHOSnZI8BjgUOH/MOUnSojGvTh9V1QNJ3g78K7AJcGpVXTPmtGB0p6tGEWchfRfjzN8Yxpn/caY0ry40S5LGa76dPpIkjZFFQZLUZ1FoIcmvOz7+g0kuH3hMDPn4leT0gfebJlmX5EvDjDNw/Fc1MXft6Pgj/T5NjE5/B0YVp+ufzUCc9yW5JsmVze/0czuIsV2S85L8OMlPknyouUFlmDEqyUkD79+d5LhhxmiOO/k34JokVyQ5KslY/j5bFOaH+6rq2QOPm4d8/N8Az0ry+Ob9S4CfDTnGoMOA79K7e6wLo/4+C0nXPxuSPA94ObBXVe0BvBi4dcgxApwLfKGqdgb+GNgMOGGYcYD7gVcn2WrIx13f5N+A3en9Pr8MOLbjmFOyKCweXwEObF4fBpzZRZAkmwEvAI6kwz88jOj7LCQj/NksBe6sqvsBqurOqhr2eKP9gN9W1SebGA8C7wTenOQJQ4zzAL27gd45xGPOqKrW0pu14e1N8Rspi8L88PiBU0f/0lGMs4BDkzwO2AP4fkdxXgl8tap+BPw8yV4dxRnV91lIRvWz+RqwfZIfJflIkhd1EGN34NLBhqq6G7gFeOaQY30YeH2SJw35uNOqqhvp/X3eelQxJ1kU5ofB00ev6iJAVV0JTND7r/rLXcRoHEbvDzbN82FdBBnh91lIRvWz+TXwJ/T+210HfDbJm4YcJqw3Bc4s7XPWFJtPA+8Y5nFbGHkvAebZ4DV17nzgA8A+wFOHffAkT6XXrX9WkqI3ALGSvKe6GRDT6fdZSEb9s2lO51wEXJTkKuBw4FNDDHEN8JrBhiRb0Jsm5ydDjDPpg8BlwCc7OPYfSPJ04EFg7SjiDbKnsLicCvxjVV3V0fFfC3y6qnasqomq2h64CXhhR/G6/j4Lych+Nkl2SbLzQNOzgZ8OOcyFwBOSvLGJuQlwEvCpqrp3yLGoqp8DZ9O7HtOpJEuAjwH/1NE/UzOyKMwiyab07kB4xKuqNVX1oQ5DHAasf03kHOAvugg2gu8DjO53oOM4o/zZbAacluTaJFfSWzDruGEGaP5Yvgo4JMmPgR8BvwXeO8w46zmJ3rTWXZi8rngN8A1612WO7yjWjJzmYhZJ9gQ+XlV7jzsXjceofgf8XdN8YE9hBkneQu9Wx38Ydy4aj1H9Dvi7pvnCnoIkqc+egiSpz6IgSeqzKEiS+iwK0ggleXKStw6836fL2V2lDWVRkEbrycBbZ91LGhOLgjSNJBNJfpjklCRXJzkjyYuT/Fszh//eSbZM8oVm3YCLk+zRfPa4JKcmuSjJjUkm581ZCTyjGah0YtO2WZLPN7HOGMfMmNIk5z6SZvZM4BB6k7v9O70RwC8EDqI3evZW4AdV9cok+9GbOO3ZzWd3BfYFNgeuT/JRYAXwrKp6NvROHwHPoTfr523Av9Gb3vq7o/hy0vrsKUgzu6mqrqqqh+hNwnZhM8XCVfRmaX0hcDpAVX0TeOrAFMsXVNX9VXUnvYnNtpkmxiXNlB0PAZc3x5XGwqIgzWxwLqKHBt4/RK+nPdWpnskRoYOffZDpe+Zt95M6Z1GQNs53gNdD/1TQnc38+9O5h97pJGle8j8SaeMcB3yymQ30XnrrBkyrqu5qLlRfTW9J0Qu6T1Fqz7mPJEl9nj6SJPVZFCRJfRYFSVKfRUGS1GdRkCT1WRQkSX0WBUlS3/8HSfpBEaC/p2IAAAAASUVORK5CYII=\n", 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" ] @@ -1790,12 +2342,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1807,7 +2359,7 @@ } ], "source": [ - "weather = read_csv('rotterdam_weather_2000_2010.txt', skiprows=11,\n", + "weather = pd.read_csv('rotterdam_weather_2000_2010.txt', skiprows=11,\n", " parse_dates=['YYYYMMDD'], index_col='YYYYMMDD', skipinitialspace=True)\n", "weather.TG = 0.1 * weather.TG\n", "weather.RH = 0.1 * weather.RH\n", @@ -1850,7 +2402,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.2" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false }, "varInspector": { "cols": { @@ -1883,5 +2453,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/notebook9_discrete_random_variables/py_exploratory_comp_9_sol.ipynb b/notebook9_discrete_random_variables/py_exploratory_comp_9_sol.ipynb index bfd9262..a8fea41 100644 --- a/notebook9_discrete_random_variables/py_exploratory_comp_9_sol.ipynb +++ b/notebook9_discrete_random_variables/py_exploratory_comp_9_sol.ipynb @@ -48,7 +48,7 @@ { "data": { "text/plain": [ - "array([1, 1, 0, 1, 0, 0, 0, 1, 1, 1])" + "array([0, 1, 1, 0, 1, 1, 1, 0, 1, 0])" ] }, "execution_count": 2, @@ -57,7 +57,7 @@ } ], "source": [ - "rnd.randint(0, 1+1, 10)" + "rnd.randint(0, 1 + 1, 10)" ] }, { @@ -75,7 +75,7 @@ { "data": { "text/plain": [ - "array([1, 0, 0, 0, 1, 1, 1, 1, 1, 1])" + "array([0, 0, 1, 0, 1, 0, 1, 1, 0, 1])" ] }, "execution_count": 3, @@ -112,7 +112,7 @@ ], "source": [ "rnd.seed(10)\n", - "rnd.randint(0, 1+1, 10)" + "rnd.randint(0, 1 + 1, 10)" ] }, { @@ -147,7 +147,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The ability to generate the exact same sequence is useful during code development. For example, by seeding the random number generator, you can compare your output to output of others trying to solve the same problem." + "The ability to generate the exact same sequence is useful during code development. For example, by seeding the random number generator, you can compare your output to output of others trying to solve the same problem. " ] }, { @@ -173,7 +173,7 @@ } ], "source": [ - "flip = rnd.randint(0, 1+1, 100)\n", + "flip = rnd.randint(0, 1 + 1, 100)\n", "headcount = 0\n", "tailcount = 0\n", "for i in range(100):\n", @@ -189,7 +189,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First of all, note that the number of heads and the number of tails add up to 100. Also, note how we counted the heads and tails. We created counters `headcount` and `tailcount`, looped through all flips, and added 1 to the appropriate counter. Instead of a loop, we could have used a condition for the indices combined with a summation as follows" + "First of all, note that the number of heads and the number of tails adds up to 100. Also, note how we counted the heads and tails. We created counters `headcount` and `tailcount`, looped through all flips, and added 1 to the appropriate counter. Instead of a loop, we could have used a condition for the indices combined with a summation as follows" ] }, { @@ -231,8 +231,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "outcome 0 is 59\n", - "outcome 1 is 41\n" + "outcome 0 is 59\n", + "outcome 1 is 41\n" ] } ], @@ -240,7 +240,7 @@ "outcomes = np.zeros(2, dtype='int') # Two outcomes. heads are stored in outcome[0], tails in outcome[1]\n", "for i in range (2):\n", " outcomes[i] = np.count_nonzero(flip == i)\n", - " print('outcome ', i, ' is ', outcomes[i])" + " print(f'outcome {i} is {outcomes[i]}')" ] }, { @@ -270,7 +270,7 @@ "metadata": {}, "source": [ "### Flipping a coin twice\n", - "Next we are going to flip a coin twice for 100 times and count the number of tails. We generate a random array of 0-s (heads) and 1-s (tails) with two rows (representing two coin flips) and 100 colums. The sum of the two rows represents the number of tails. The `np.sum` function takes an array and by default sums all the values in the array and returns one number. In this case we want to sum the rows. For that, the `sum` function has a keyword argument called `axis`, where `axis=0` sums over index 0 of the array (the rows), `axis=1` sums over the index 1 of the array (the columns), etc." + "Next, we are going to flip a coin twice for 100 times and count the number of tails in two throws. We generate a random array of 0-s (heads) and 1-s (tails) with two rows (representing two coin flips) and 100 colums. The sum of the two rows represents the number of tails. Recall that the `np.sum` function takes an array as input argument and by default sums all the values in the array and returns one number. In this case we want to sum the rows. For that, the `sum` function has a keyword argument called `axis`, where `axis=0` sums over index 0 of the array (the rows), `axis=1` sums over the index 1 of the array (the columns), etc." ] }, { @@ -288,7 +288,7 @@ ], "source": [ "rnd.seed(55)\n", - "flips = rnd.randint(low=0, high=1+1, size=(2, 100))\n", + "flips = rnd.randint(low=0, high=1 + 1, size=(2, 100))\n", "tails = np.sum(flips, axis=0)\n", "number_of_tails = np.zeros(3, dtype='int')\n", "for i in range(3):\n", @@ -321,7 +321,7 @@ ], "source": [ "rnd.seed(55)\n", - "flips1 = rnd.randint(low=0, high=1+1, size=5)\n", + "flips1 = rnd.randint(low=0, high=1 + 1, size=5)\n", "rnd.seed(55)\n", "flips2 = rnd.choice(range(2), size=5, replace=True)\n", "np.alltrue(flips1 == flips2) # Check whether all values in the two arrays are equal" @@ -342,7 +342,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] @@ -365,7 +365,7 @@ "metadata": {}, "source": [ "### Cumulative Probability\n", - "Next we compute the experimental probability of 0 tails, 1 tail, and 2 tails through division by the total number of trials (one trial is two coin flips). The three probabilities add up to 1. The cumulative probability distribution is obtained by cumulatively summing the probabilities using the `cumsum` function of `numpy`. The first value is the probability of throwing 0 tails. The second value is the probability of 1 or fewer tails, and the third value it the probability of 2 or fewer tails. The probability is computed as the number of tails divided by the total number of trials." + "Next, we compute the experimental probability of 0 tails, 1 tail, and 2 tails through division by the total number of trials (one trial is two coin flips). The three probabilities add up to 1. The cumulative probability distribution is obtained by cumulatively summing the probabilities using the `cumsum` function of `numpy`. The first value is the probability of throwing 0 tails. The second value is the probability of 1 or fewer tails, and the third value it the probability of 2 or fewer tails. The probability is computed as the number of tails divided by the total number of trials." ] }, { @@ -401,7 +401,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -424,7 +424,7 @@ "metadata": {}, "source": [ "### Exercise 2. Flip a coin five times\n", - "Flip a coin five times in a row and record how many times you obtain tails (varying from 0-5). Perform the exeriment 1000 times. Make a bar graph with the total number of tails on the horizontal axis and the emperically computed probability to get that many tails, on the vertical axis. Execute your code several times (hit [shift]-[enter]) and see that the graph changes a bit every time, as the sequence of random numbers changes every time. " + "Flip a coin five times in a row and record how many times you obtain tails (varying from 0-5). Perform the experiment 1000 times. Make a bar graph with the total number of tails on the horizontal axis and the experimentally computed probability to get that many tails, on the vertical axis. Execute your code several times (hit [shift]-[enter]) and see that the graph changes a bit every time, as the sequence of random numbers changes every time. " ] }, { @@ -535,8 +535,10 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [] }, { @@ -564,7 +566,12 @@ "output_type": "stream", "text": [ "balls: [0 0 0 0 1 1 1 1 1 1]\n", - "drawing: [1 1 1 0 1 0 0 1 1 0]\n", + "drawing 10 balls with replacement\n", + "drawing: [1 1 1 1 1 0 1 1 1 0]\n", + "blue balls: 2\n", + "red balls: 8\n", + "drawing 10 balls without replacement\n", + "drawing: [0 0 1 1 0 1 0 1 1 1]\n", "blue balls: 4\n", "red balls: 6\n" ] @@ -574,12 +581,77 @@ "balls = np.zeros(10, dtype='int') # zero is blue\n", "balls[4:] = 1 # one is red\n", "print('balls:', balls)\n", + "print('drawing 10 balls with replacement')\n", + "rnd.seed(77)\n", "drawing = rnd.choice(balls, 10, replace=True)\n", "print('drawing:', drawing)\n", "print('blue balls:', np.count_nonzero(drawing == 0))\n", + "print('red balls:', np.count_nonzero(drawing == 1))\n", + "print('drawing 10 balls without replacement')\n", + "rnd.seed(77)\n", + "drawing = rnd.choice(balls, 10, replace=False)\n", + "print('drawing:', drawing)\n", + "print('blue balls:', np.count_nonzero(drawing == 0))\n", + "print('red balls:', np.count_nonzero(drawing == 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous example, we generated an example with 4 blue balls (zeros) and 6 red balls (ones), which was easy. But you can see this gets cumbersome when we have 4 million blue balls and 6 million red balls. But luckily, the `choice` function has an alternative: you can specifiy a sequence of values, and for each value you can specify the probability that it is drawn. Obviously, the `replace=False` argument doesn't make much sense anymore when the probabilities are specified (as the probabilities change when balls are not replaced). Repeating the previous example with replacement by specifying probabilities gives" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drawing: [1 0 1 0 1 1 1 1 1 0]\n", + "blue balls: 3\n", + "red balls: 7\n" + ] + } + ], + "source": [ + "drawing = rnd.choice([0, 1], 10, p=[0.4, 0.6]) # replace=False by default\n", + "print('drawing:', drawing)\n", + "print('blue balls:', np.count_nonzero(drawing == 0))\n", "print('red balls:', np.count_nonzero(drawing == 1))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another cool feature of the `choice` function is that it also works on lists. For example, to pick 3 names at random from a list of 11 gives:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Daley', 'Quincy', 'Donny'], dtype='" ] @@ -702,7 +774,7 @@ ], "source": [ "N = 1000\n", - "tails = np.sum(rnd.randint(0, 1+1, (5, 1000)), axis=0)\n", + "tails = np.sum(rnd.randint(0, 1 + 1, (5, 1000)), axis=0)\n", "counttails = np.zeros(6, dtype='int')\n", "for i in range(6):\n", " counttails[i] = np.count_nonzero(tails == i)\n", @@ -713,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -725,7 +797,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -755,7 +827,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -763,29 +835,26 @@ "output_type": "stream", "text": [ "Theoretical probabilities:\n", - "0 tails 0.03125\n", - "1 tails 0.15625\n", - "2 tails 0.3125\n", - "3 tails 0.3125\n", - "4 tails 0.15625\n", - "5 tails 0.03125\n", - "Probability with 1000 trials: [0.031 0.16 0.307 0.334 0.139 0.029]\n", - "Probability with 10000 trials: [0.0315 0.1579 0.3083 0.3098 0.1634 0.0291]\n", - "Probability with 100000 trials: [0.03093 0.15591 0.31454 0.31266 0.15484 0.03112]\n" + "Exact probability: [0.03125 0.15625 0.3125 0.3125 0.15625 0.03125]\n", + "Probability with 1000 trials: [0.031 0.16 0.307 0.334 0.139 0.029]\n", + "Probability with 10000 trials: [0.0315 0.1579 0.3083 0.3098 0.1634 0.0291]\n", + "Probability with 100000 trials: [0.03093 0.15591 0.31454 0.31266 0.15484 0.03112]\n" ] } ], "source": [ "from scipy.special import comb\n", "print('Theoretical probabilities:')\n", + "pexact = np.empty(6)\n", "for k in range(6):\n", - " print(k, ' tails ', comb(5, k) * 0.5**k * 0.5**(5 - k))\n", + " pexact[k] = comb(5, k) * 0.5**k * 0.5**(5 - k)\n", + "print('Exact probability:', pexact)\n", "for N in (1000, 10000, 100000):\n", " tails = np.sum(rnd.randint(0, 1+1, (5, N)), axis=0)\n", " counttails = np.zeros(6)\n", " for i in range(6):\n", " counttails[i] = np.count_nonzero(tails==i)\n", - " print('Probability with', N, 'trials: ', counttails / float(N))" + " print(f'Probability with {N} trials: {counttails / N}')" ] }, { @@ -799,14 +868,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -818,11 +887,11 @@ } ], "source": [ - "dice = rnd.randint(1, 6+1, (2, 1000))\n", + "dice = rnd.randint(1, 6 + 1, (2, 1000))\n", "highest_dice = np.max(dice, 0)\n", "outcome = np.zeros(6)\n", "for i in range(6):\n", - " outcome[i] = np.sum(highest_dice == i+1) / 1000\n", + " outcome[i] = np.sum(highest_dice == i + 1) / 1000\n", "plt.bar(np.arange(1, 7), height=outcome, width=1)\n", "plt.xlabel('highest dice in two throws')\n", "plt.ylabel('probability');" @@ -839,7 +908,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -855,11 +924,11 @@ ], "source": [ "for N in [100, 1000, 10000]:\n", - " dice = rnd.randint(1, 6+1, (2, N))\n", + " dice = rnd.randint(1, 6 + 1, (2, N))\n", " highest_dice = np.max(dice, axis=0)\n", " outcome = np.zeros(6)\n", " for i in range(6):\n", - " outcome[i] = np.sum(highest_dice == i+1) / N\n", + " outcome[i] = np.sum(highest_dice == i + 1) / N\n", " print('Outcome for', N, 'throws: ', outcome)\n", "# Exact values\n", "exact = np.zeros(6)\n", @@ -879,23 +948,21 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "polled for A: 508\n", + "polled for A: 522\n", "The Dog will predict the wrong winner\n" ] } ], "source": [ "rnd.seed(2)\n", - "people = np.zeros(1000000, dtype='int') # candidate A is 0\n", - "people[490000:] = 1 # candidate B is 1\n", - "poll = rnd.choice(people, 1000)\n", + "poll = rnd.choice([0, 1], 1000, p=[0.49, 0.51]) # A=0, B=1\n", "polled_for_A = np.count_nonzero(poll == 0)\n", "print('polled for A:', polled_for_A)\n", "if polled_for_A > 500: \n", @@ -906,7 +973,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -914,7 +981,7 @@ "output_type": "stream", "text": [ "1000 polls of 1000 people\n", - "Probability that The Dog predicts candidate A to win: 0.267\n" + "Probability that The Dog predicts candidate A to win: 0.262\n" ] } ], @@ -922,9 +989,7 @@ "Awins = 0\n", "Bwins = 0\n", "for i in range(1000):\n", - " people = np.zeros(1000000, dtype='int') # candidate A is 0\n", - " people[490000:] = 1 # candidate B is 1\n", - " poll = rnd.choice(people, 1000)\n", + " poll = rnd.choice([0, 1], 1000, p=[0.49, 0.51])\n", " polled_for_A = np.count_nonzero(poll == 0)\n", " if polled_for_A > 500: \n", " Awins += 1\n", @@ -936,7 +1001,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -944,7 +1009,7 @@ "output_type": "stream", "text": [ "1000 polls of 5000 people\n", - "Probability that The Dog predicts candidate A to win: 0.07\n" + "Probability that The Dog predicts candidate A to win: 0.071\n" ] } ], @@ -952,9 +1017,7 @@ "Awins = 0\n", "Bwins = 0\n", "for i in range(1000):\n", - " people = np.zeros(1000000, dtype='int') # candidate A is 0\n", - " people[490000:] = 1 # candidate B is 1\n", - " poll = rnd.choice(people, 5000)\n", + " poll = rnd.choice([0, 1], 5000, p=[0.49, 0.51])\n", " polled_for_A = np.count_nonzero(poll == 0)\n", " if polled_for_A > 2500: \n", " Awins += 1\n", @@ -988,7 +1051,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.4" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false } }, "nbformat": 4, diff --git a/pythonjl_video1.ipynb b/pythonjl_video1.ipynb new file mode 100644 index 0000000..4bddcc3 --- /dev/null +++ b/pythonjl_video1.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2 * 3" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the value of c is: 6\n" + ] + } + ], + "source": [ + "a = 2\n", + "b = 3\n", + "c = a * b\n", + "print('the value of c is:', c)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 3\n" + ] + } + ], + "source": [ + "print(a, b)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "print the value of a is 2, and a / b equals: 6.6667e-01\n" + ] + } + ], + "source": [ + "d = a / b\n", + "print(f'print the value of a is {a}, and a / b equals: {d:.4e}')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + } + ], + "source": [ + "x = 3\n", + "y = (x - 1) * (x + 1)\n", + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5403023058681398" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.cos(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.linspace(-4, 4, 100)\n", + "y = (x - 1) * (x + 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, y, 'g', label='a green line')\n", + "plt.xlabel('this is the x-label')\n", + "plt.ylabel('y-label goes here')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}