diff --git a/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb b/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb index 8e59aafb4..d1042e0d3 100644 --- a/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb +++ b/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb @@ -1790,12 +1790,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1811,7 +1811,7 @@ "from sklearn.linear_model import LinearRegression\n", "\n", "x = np.random.rand(100,1)\n", - "y = 2*x+np.random.randn(100,1)\n", + "y = 2*x+0.01*np.random.randn(100,1)\n", "linreg = LinearRegression()\n", "linreg.fit(x,y)\n", "xnew = np.array([[0],[1]])\n", @@ -1923,12 +1923,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1943,7 +1943,7 @@ "from sklearn.linear_model import LinearRegression\n", "\n", "x = np.random.rand(100,1)\n", - "y = 5*x+0.01*np.random.randn(100,1)\n", + "y = 5*x+np.random.randn(100,1)\n", "linreg = LinearRegression()\n", "linreg.fit(x,y)\n", "ypredict = linreg.predict(x)\n", @@ -1976,9 +1976,34 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The intercept alpha: \n", + " [2.]\n", + "Coefficient beta : \n", + " [[5.]]\n", + "Mean squared error: 0.00\n", + "Variance score: 1.00\n", + "Mean squared log error: 0.00\n", + "Mean absolute error: 0.00\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt \n", @@ -1986,7 +2011,7 @@ "from sklearn.metrics import mean_squared_error, r2_score, mean_squared_log_error, mean_absolute_error\n", "\n", "x = np.random.rand(100,1)\n", - "y = 2.0+ 5*x+0.5*np.random.randn(100,1)\n", + "y = 2.0+ 5*x#+0.05*np.random.randn(100,1)\n", "linreg = LinearRegression()\n", "linreg.fit(x,y)\n", "ypredict = linreg.predict(x)\n", @@ -2325,7 +2350,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -2373,7 +2398,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -2434,7 +2459,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -2476,7 +2501,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -2570,7 +2595,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -2592,7 +2617,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -2610,9 +2635,31 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean squared error: 0.04\n", + "Variance score: 0.95\n", + "Mean absolute error: 0.05\n", + "[ 0.00000000e+00 7.06492086e-03 -1.73091052e-01 -1.66020213e+01\n", + " 1.17385778e+00] 15.212327334149492\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# The mean squared error \n", "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, fity))\n", @@ -2647,9 +2694,92 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " N Z A Element Ebinding Eapprox\n", + "A \n", + "1 0 0 1 1 H 0.000000 0.000000\n", + "2 1 1 1 2 H 1.112283 1.112283\n", + "3 2 2 1 3 H 2.827265 2.827265\n", + "4 6 2 2 4 He 7.073915 7.073915\n", + "5 9 3 2 5 He 5.512132 5.512132\n", + "6 14 3 3 6 Li 5.332331 5.332331\n", + "7 19 4 3 7 Li 5.606439 5.606439\n", + "8 24 4 4 8 Be 7.062435 7.062435\n", + "9 29 5 4 9 Be 6.462668 6.462668\n", + "10 34 6 4 10 Be 6.497630 6.497630\n", + "11 40 6 5 11 B 6.927732 6.927732\n", + "12 46 6 6 12 C 7.680144 7.680144\n", + "13 52 7 6 13 C 7.469849 7.469849\n", + "14 57 8 6 14 C 7.520319 7.520319\n", + "15 64 8 7 15 N 7.699460 7.699460\n", + "16 72 8 8 16 O 7.976206 7.976206\n", + "17 78 9 8 17 O 7.750728 7.750728\n", + "18 85 10 8 18 O 7.767097 7.773058\n", + "19 93 10 9 19 F 7.779018 7.773058\n", + "20 102 10 10 20 Ne 8.032240 8.032240\n", + "21 110 11 10 21 Ne 7.971713 7.971713\n", + "22 118 12 10 22 Ne 8.080465 8.080465\n", + "23 128 12 11 23 Na 8.111493 8.111493\n", + "24 137 12 12 24 Mg 8.260709 8.260709\n", + "25 146 13 12 25 Mg 8.223502 8.223502\n", + "26 154 14 12 26 Mg 8.333870 8.333870\n", + "27 164 14 13 27 Al 8.331553 8.331553\n", + "28 174 14 14 28 Si 8.447744 8.447744\n", + "29 183 15 14 29 Si 8.448635 8.448635\n", + "30 192 16 14 30 Si 8.520654 8.520654\n", + "... ... ... ... ... ... ...\n", + "238 3089 146 92 238 U 7.570125 7.573113\n", + "239 3099 146 93 239 Np 7.560567 7.558304\n", + "240 3109 146 94 240 Pu 7.556042 7.558304\n", + "241 3118 147 94 241 Pu 7.546439 7.546439\n", + "242 3127 148 94 242 Pu 7.541327 7.541327\n", + "243 3136 149 94 243 Pu 7.531008 7.527912\n", + "244 3144 150 94 244 Pu 7.524815 7.527912\n", + "245 3154 149 96 245 Cm 7.515767 7.513619\n", + "246 3162 150 96 246 Cm 7.511471 7.513619\n", + "247 3170 151 96 247 Cm 7.501931 7.499329\n", + "248 3177 152 96 248 Cm 7.496728 7.499329\n", + "249 3186 152 97 249 Bk 7.486040 7.482998\n", + "250 3194 152 98 250 Cf 7.479956 7.482998\n", + "251 3201 153 98 251 Cf 7.470500 7.470500\n", + "252 3209 154 98 252 Cf 7.465347 7.465347\n", + "253 3216 155 98 253 Cf 7.454829 7.452027\n", + "254 3224 156 98 254 Cf 7.449225 7.452027\n", + "255 3232 156 99 255 Es 7.437821 7.434800\n", + "256 3241 156 100 256 Fm 7.431780 7.434800\n", + "257 3248 157 100 257 Fm 7.422194 7.422194\n", + "258 3256 157 101 258 Md 7.409675 7.409675\n", + "259 3264 157 102 259 No 7.399974 7.399974\n", + "260 3275 154 106 260 Sg 7.342562 7.342562\n", + "261 3280 157 104 261 Rf 7.371384 7.371384\n", + "262 3289 156 106 262 Sg 7.341185 7.341185\n", + "264 3304 156 108 264 Hs 7.298375 7.298375\n", + "265 3310 157 108 265 Hs 7.296247 7.297260\n", + "266 3317 158 108 266 Hs 7.298273 7.297260\n", + "269 3338 159 110 269 Ds 7.250154 7.250154\n", + "270 3344 160 110 270 Ds 7.253775 7.253775\n", + "\n", + "[267 rows x 6 columns]\n", + "0.009883615646716182\n" + ] + } + ], "source": [ "\n", "#Decision Tree Regression\n", @@ -2695,9 +2825,101 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/matplotlib/__init__.py:886: MatplotlibDeprecationWarning: \n", + "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n", + " \"found relative to the 'datapath' directory.\".format(key))\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from sklearn.neural_network import MLPRegressor\n", "from sklearn.metrics import accuracy_score\n", diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs000.html b/doc/pub/Intro2Course/html/._Intro2Course-bs000.html index bed2b5d50..db4dc7811 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs000.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs000.html @@ -129,7 +129,7 @@ end of tocinfo -->
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

-

Aug 21, 2019

+

Aug 22, 2019


diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs005.html b/doc/pub/Intro2Course/html/._Intro2Course-bs005.html index fac66aa2b..ee69b75ea 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs005.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs005.html @@ -120,7 +120,7 @@ end of tocinfo -->

  1. Project 1: September 30 (graded with feedback)
  2. Project 2: November 4 (graded with feedback)
    3. -
  3. Project 3: December 2 (graded with feedback)
    4. +
  4. Project 3: December 13 (graded with feedback)
Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry. diff --git a/doc/pub/Intro2Course/html/Intro2Course-bs.html b/doc/pub/Intro2Course/html/Intro2Course-bs.html index bed2b5d50..db4dc7811 100644 --- a/doc/pub/Intro2Course/html/Intro2Course-bs.html +++ b/doc/pub/Intro2Course/html/Intro2Course-bs.html @@ -129,7 +129,7 @@ end of tocinfo -->
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

-

Aug 21, 2019

+

Aug 22, 2019


diff --git a/doc/pub/Intro2Course/html/Intro2Course-reveal.html b/doc/pub/Intro2Course/html/Intro2Course-reveal.html index 60958f31d..53faab157 100644 --- a/doc/pub/Intro2Course/html/Intro2Course-reveal.html +++ b/doc/pub/Intro2Course/html/Intro2Course-reveal.html @@ -132,7 +132,7 @@ td.padding {

[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

 
-

Aug 21, 2019

+

Aug 22, 2019


@@ -260,7 +260,7 @@ td.padding {

  1. Project 1: September 30 (graded with feedback)

  2. Project 2: November 4 (graded with feedback)
    3. -

  3. Project 3: December 2 (graded with feedback)
    4. +

  4. Project 3: December 13 (graded with feedback)

diff --git a/doc/pub/Intro2Course/html/Intro2Course-solarized.html b/doc/pub/Intro2Course/html/Intro2Course-solarized.html index 82d2102fb..b59cdd1b8 100644 --- a/doc/pub/Intro2Course/html/Intro2Course-solarized.html +++ b/doc/pub/Intro2Course/html/Intro2Course-solarized.html @@ -109,7 +109,7 @@ end of tocinfo -->

[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

-

Aug 21, 2019

+

Aug 22, 2019












@@ -227,7 +227,7 @@ end of tocinfo -->

  1. Project 1: September 30 (graded with feedback)
  2. Project 2: November 4 (graded with feedback)
    3. -
  3. Project 3: December 2 (graded with feedback)
    4. +
  4. Project 3: December 13 (graded with feedback)
Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry. diff --git a/doc/pub/Intro2Course/html/Intro2Course.html b/doc/pub/Intro2Course/html/Intro2Course.html index 53740e87d..aad62092d 100644 --- a/doc/pub/Intro2Course/html/Intro2Course.html +++ b/doc/pub/Intro2Course/html/Intro2Course.html @@ -114,7 +114,7 @@ end of tocinfo -->
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

-

Aug 21, 2019

+

Aug 22, 2019












@@ -232,7 +232,7 @@ end of tocinfo -->

  1. Project 1: September 30 (graded with feedback)
  2. Project 2: November 4 (graded with feedback)
    3. -
  3. Project 3: December 2 (graded with feedback)
    4. +
  4. Project 3: December 13 (graded with feedback)
Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry. diff --git a/doc/pub/Intro2Course/ipynb/Intro2Course.ipynb b/doc/pub/Intro2Course/ipynb/Intro2Course.ipynb index fe7e0bb1c..e90d02eff 100644 --- a/doc/pub/Intro2Course/ipynb/Intro2Course.ipynb +++ b/doc/pub/Intro2Course/ipynb/Intro2Course.ipynb @@ -10,7 +10,7 @@ " \n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", "\n", - "Date: **Aug 21, 2019**\n", + "Date: **Aug 22, 2019**\n", "\n", "Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", @@ -110,7 +110,7 @@ "\n", "2. Project 2: November 4 (graded with feedback)\n", "\n", - "3. Project 3: December 2 (graded with feedback)\n", + "3. Project 3: December 13 (graded with feedback)\n", "\n", "Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.\n", "\n", diff --git a/doc/pub/Intro2Course/ipynb/ipynb-Intro2Course-src.tar.gz b/doc/pub/Intro2Course/ipynb/ipynb-Intro2Course-src.tar.gz index 061b8dcc2..07e14df40 100644 Binary files a/doc/pub/Intro2Course/ipynb/ipynb-Intro2Course-src.tar.gz and b/doc/pub/Intro2Course/ipynb/ipynb-Intro2Course-src.tar.gz differ diff --git a/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf b/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf index 2728f68a7..8f89fc494 100644 Binary files a/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf and b/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf differ diff --git a/doc/src/Intro2Course/Intro2Course.do.txt b/doc/src/Intro2Course/Intro2Course.do.txt index 8520a144e..8ba0dfbbd 100644 --- a/doc/src/Intro2Course/Intro2Course.do.txt +++ b/doc/src/Intro2Course/Intro2Course.do.txt @@ -80,7 +80,7 @@ o "Stian Bilek":"https://www.researchgate.net/profile/Stian_Bilek" o Project 1: September 30 (graded with feedback) o Project 2: November 4 (graded with feedback) -o Project 3: December 2 (graded with feedback) +o Project 3: December 13 (graded with feedback) Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.