From 511d33903986697a0bc2cd3f0f79e2345f58f32b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=81lex=20S=C3=A1ez?= Date: Sun, 3 Apr 2016 20:05:08 +0200 Subject: [PATCH] Analysing air quality data for Madrid --- Basic Python Packages for Science.ipynb | 764 +++++++++++++++++++++++- data/barrio_del_pilar-20151222.csv | 195 ++++++ data/barrio_del_pilar-20160322.csv | 195 ++++++ 3 files changed, 1124 insertions(+), 30 deletions(-) create mode 100644 data/barrio_del_pilar-20151222.csv create mode 100644 data/barrio_del_pilar-20160322.csv diff --git a/Basic Python Packages for Science.ipynb b/Basic Python Packages for Science.ipynb index a58eb60..ef7fdeb 100644 --- a/Basic Python Packages for Science.ipynb +++ b/Basic Python Packages for Science.ipynb @@ -193,7 +193,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1000 loops, best of 3: 1.62 ms per loop\n" + "100 loops, best of 3: 1.82 ms per loop\n" ] } ], @@ -213,7 +213,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "10000 loops, best of 3: 98.6 µs per loop\n" + "The slowest run took 14.38 times longer than the fastest. This could mean that an intermediate result is being cached.\n", + "10000 loops, best of 3: 95.7 µs per loop\n" ] } ], @@ -546,6 +547,50 @@ "chess_board" ] }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.matshow(chess_board, cmap=plt.cm.gray)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -555,39 +600,94 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "x = np.linspace(0, 10)\n", + "x = np.linspace(1, 10)\n", "y = np.sin(x)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/miniconda/envs/pydata-basic-python/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log\n", - " if __name__ == '__main__':\n" - ] + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, y_2, 'r-*')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, "metadata": { "collapsed": false }, @@ -600,7 +700,7 @@ " [33, 16, 91]])" ] }, - "execution_count": 20, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -615,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -628,7 +728,7 @@ " [10497, 9813, 11910]])" ] }, - "execution_count": 21, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -639,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -650,7 +750,7 @@ "array([ 240.4, 250.8, 783.1])" ] }, - "execution_count": 22, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -663,7 +763,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -676,7 +776,7 @@ " [-0.03598099, -0.05634759, 0.03394433]])" ] }, - "execution_count": 23, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -687,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -701,7 +801,7 @@ " [-0.92335283, 0.1138173 , 0.60198985]]))" ] }, - "execution_count": 24, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -714,36 +814,574 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. Graphical Representation: matplotlib" + "#### Air quality data " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import HTML\n", + "HTML('')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "![matplotlib](./static/matplotlib.png)" + "##### Loading the data " ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estación: Barrio del Pilar;;;;\r\n", + "Fecha;Hora;CO;NO2;O3\r\n", + ";;mg/m³;µg/m³;µg/m³\r\n", + "22/03/2016;01:00;0.2;14;73\r\n", + "22/03/2016;02:00;0.2;10;77\r\n", + "22/03/2016;03:00;0.2;9;75\r\n", + "22/03/2016;04:00;0.2;3;81\r\n", + "22/03/2016;05:00;0.2;3;81\r\n", + "22/03/2016;06:00;0.2;6;79\r\n", + "22/03/2016;07:00;0.2;24;59\r\n" + ] + } + ], + "source": [ + "# Linux command \n", + "!head ./data/barrio_del_pilar-20160322.csv\n", + "\n", + "# Windows\n", + "# !gc log.txt | select -first 10 # head" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.2, 14. , 73. ],\n", + " [ 0.2, 10. , 77. ],\n", + " [ 0.2, 9. , 75. ],\n", + " [ 0.2, 3. , 81. ],\n", + " [ 0.2, 3. , 81. ],\n", + " [ 0.2, 6. , 79. ],\n", + " [ 0.2, 24. , 59. ],\n", + " [ 0.3, 48. , 37. ],\n", + " [ 0.3, 40. , 43. ],\n", + " [ 0.3, 41. , 44. ],\n", + " [ 0.3, 20. , 68. ],\n", + " [ 0.3, 17. , 74. ],\n", + " [ 0.2, 14. , 84. ],\n", + " [ 0.3, 16. , 88. ],\n", + " [ 0.3, 15. , 94. ],\n", + " [ 0.4, 29. , 81. ],\n", + " [ 0.3, 23. , 82. ],\n", + " [ 0.3, 26. , 81. ],\n", + " [ 0.3, 30. , 75. ],\n", + " [ 0.4, 57. , 39. ],\n", + " [ 0.4, 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+ ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Dealing with missing values " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ nan, nan, nan])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(data1, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.33717277, 29.79581152, 55.47643979])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.nanmean(data1, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "masked_array(data = [0.3371727748691094 29.79581151832461 55.47643979057592],\n", + " mask = [False False False],\n", + " fill_value = 1e+20)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data1 = np.ma.masked_invalid(data1)\n", + "np.mean(data1, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "%matplotlib notebook\n", - "import matplotlib.pyplot as plt" + "data2 = np.genfromtxt('./data/barrio_del_pilar-20151222.csv', skip_header=3, delimiter=';', usecols=(2,3,4))\n", + "data2 = np.ma.masked_invalid(data2)" ] }, { "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Plotting the data " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Maximum values ** from: http://www.mambiente.munimadrid.es/opencms/export/sites/default/calaire/Anexos/valores_limite_1.pdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* NO2\n", + " - Media anual: 40 µg/m3\n", + " - **Media horaria: 200 µg/m3 **" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 220)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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JvsVqwRg35rI6kS/wyTZvBcJoNgKgAiTHwOgAtEQrK1RCpMSlqDIBqtvcDcOQ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\nBoPB0Iowom8wGAytiP8DZhkyA9miEikAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(data1[:, 1], label='2016')\n", + "plt.plot(data2[:, 1], label='2015')\n", + "\n", + "plt.legend()\n", + "\n", + "plt.hlines(200, 0, 200, linestyles='--')\n", + "plt.ylim(0, 220)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import HTML\n", + "HTML('')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* CO \n", + " - **Máxima diaria de las medias móviles octohorarias: 10 mg/m³**" + ] + }, + { + "cell_type": "code", + "execution_count": 38, "metadata": { "collapsed": true }, + "outputs": [], "source": [ - "#### Other options... \n", - "\n" + "# http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.convolve.html\n", + "def moving_average(x, N=8):\n", + " return np.convolve(x, np.ones(N)/N, mode='same')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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fTqDXquUE1ahRcMUVZ/fO69d3vlC1ajnj0XPnQlSUU26LFs5/CwqcXyEFBdCq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\nAJPio4yvFU8AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(moving_average(data1[:, 0]), label='2016')\n", + "#plt.plot(data1[:, 0])\n", + "\n", + "plt.plot(moving_average(data2[:, 0]), label='2015')\n", + "#plt.plot(data2[:, 0])\n", + "\n", + "plt.hlines(10, 0, 250, linestyles='--')\n", + "plt.ylim(0, 11)\n", + "\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* O3\n", + " - **Máxima diaria de las medias móviles octohorarias: 120 µg/m3**\n", + " - Umbral de información. 180 µg/m3\n", + " - Media horaria. Umbral de alerta. 240 µg/m3" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/miniconda/envs/pydata-basic-python/lib/python3.5/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", + " warnings.warn(\"No labelled objects found. \"\n" + ] + }, + { + "data": { + "image/png": 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2Qfad7UnXkzBk6RC80+8d3B1w953tz3Z9FmMjxuL5dc/b/YnrRt4N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\nxhjTEQ59xhjTEQ59xhjTkf8Pth7JNHEzhb4AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(moving_average(data1[:, 2]))\n", + "#plt.plot(data1[:, 2])\n", + "\n", + "plt.plot(moving_average(data2[:, 2]))\n", + "#plt.plot(data2[:, 2])\n", + "\n", + "plt.hlines(180, 0, 250, linestyles='--')\n", + "plt.ylim(0, 190)\n", + "\n", + "plt.legend()" ] }, { @@ -760,6 +1398,72 @@ "![matplotlib](./static/scipy_logo.png)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "scipy.linalg: ATLAS LAPACK and BLAS libraries\n", + "\n", + "scipy.stats: distributions, statistical functions...\n", + "\n", + "scipy.integrate: integration of functions and ODEs\n", + "\n", + "scipy.optimization: local and global optimization, fitting, root finding...\n", + "\n", + "scipy.interpolate: interpolation, splines...\n", + "\n", + "scipy.fftpack: Fourier trasnforms\n", + "\n", + "scipy.signal, scipy.special, scipy.io\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Graphical Representation: matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![matplotlib](./static/matplotlib.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib notebook\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "#### Other options... \n", + "\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -879,7 +1583,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 42, "metadata": { "collapsed": false }, @@ -1034,7 +1738,7 @@ "" ] }, - "execution_count": 26, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } diff --git a/data/barrio_del_pilar-20151222.csv b/data/barrio_del_pilar-20151222.csv new file mode 100644 index 0000000..4c6a106 --- /dev/null +++ b/data/barrio_del_pilar-20151222.csv @@ -0,0 +1,195 @@ 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