From 15e8574e2ea457c7aefcdda807b8c0c0724beaa5 Mon Sep 17 00:00:00 2001 From: Roger Labbe Date: Mon, 29 Jan 2018 09:37:23 -0800 Subject: [PATCH] Text used the wrong parameter name for hist() call, and I misattributed a SciPy function to NumPy. --- .../Computing_and_plotting_PDFs.ipynb | 197 ++++++------------ 1 file changed, 69 insertions(+), 128 deletions(-) diff --git a/Supporting_Notebooks/Computing_and_plotting_PDFs.ipynb b/Supporting_Notebooks/Computing_and_plotting_PDFs.ipynb index fbd94f0..ac4fe99 100644 --- a/Supporting_Notebooks/Computing_and_plotting_PDFs.ipynb +++ b/Supporting_Notebooks/Computing_and_plotting_PDFs.ipynb @@ -3,9 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -14,52 +12,18 @@ "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n", "@import url('http://fonts.googleapis.com/css?family=Lora');\n", "\n", - "//@import url('http://fonts.googleapis.com/css?family=Open+Sans');\n", - "//@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n", - "//@import url('http://fonts.googleapis.com/css?family=Karla');\n", - "//@import url('http://fonts.googleapis.com/css?family=Poppins');\n", - "//@import url('http://fonts.googleapis.com/css?family=Arimo');\n", - "//@import url('http://fonts.googleapis.com/css?family=Roboto');\n", - "//@import url('http://fonts.googleapis.com/css?family=Lato');\n", - "//@import url('http://fonts.googleapis.com/css?family=Domine');\n", - "//@import url('http://fonts.googleapis.com/css?family=Chivo');\n", - "//@import url('http://fonts.googleapis.com/css?family=Cardo');\n", - "//@import url('http://fonts.googleapis.com/css?family=Arvo');\n", - "//@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n", - "//@import url('http://fonts.googleapis.com/css?family=Ubuntu');\n", - "//@import url('http://fonts.googleapis.com/css?family=Fontin');\n", - "//@import url('http://fonts.googleapis.com/css?family=Raleway');\n", - "//@import url('http://fonts.googleapis.com/css?family=Merriweather');\n", - "\n", - "\n", ".CodeMirror pre {\n", " font-family: 'Source Code Pro', Consolas, monocco, monospace;\n", "}\n", " div.cell{\n", - " width: 850px;\n", + " //width: 950px;\n", " margin-left: 0% !important;\n", " margin-right: auto;\n", " }\n", " div.text_cell_render{\n", " font-family: 'Lora';\n", - " //font-family: 'Open Sans';\n", - " //font-family: 'Karla',verdana,arial,sans-serif;\n", - " //font-family: 'Roboto',verdana,arial,sans-serif;\n", - " //font-family: 'Lato',verdana,arial,sans-serif;\n", - " //font-family: 'Domine',verdana,arial,sans-serif;\n", - " //font-family: 'Chivo',verdana,arial,sans-serif;\n", - " //font-family: 'Cardo',verdana,arial,sans-serif;\n", - " //font-family: 'Arvo',verdana,arial,sans-serif;\n", - " //font-family: 'Poppins',verdana,arial,sans-serif; \n", - " //font-family: 'Ubuntu',verdana,arial,sans-serif;\n", - " //font-family: 'Fontin',verdana,arial,sans-serif;\n", - " //font-family: 'Raleway',verdana,arial,sans-serif;\n", - " //font-family: 'Merriweather',verdana,arial,sans-serif;\n", - " //font-family: 'Crimson Text', verdana,arial,sans-serif;\n", - " //font-family: verdana,arial,sans-serif;\n", - " //font-family: arial,sans-serif;\n", " line-height: 125%;\n", - " font-size: 130%;\n", + " font-size: 100%;\n", " text-align: justify;\n", " text-justify:inter-word;\n", " }\n", @@ -67,17 +31,16 @@ " background: transparent;\n", " color: #000000;\n", " font-weight: 400;\n", - " font-size: 12pt;\n", - " //font-style: bold;\n", + " font-size: 11pt;\n", " font-family: 'Source Code Pro', Consolas, monocco, monospace;\n", " }\n", " h1 {\n", " font-family: 'Open sans',verdana,arial,sans-serif;\n", "\t}\n", - "\t\n", + "\n", " div.input_area {\n", " background: #F6F6F9;\n", - " border: 1px solid #586e75; \n", + " border: 1px solid #586e75;\n", " }\n", "\n", " .text_cell_render h1 {\n", @@ -90,7 +53,7 @@ " display: block;\n", " white-space: wrap;\n", " text-align: left;\n", - " } \n", + " }\n", " h2 {\n", " font-family: 'Open sans',verdana,arial,sans-serif;\n", " text-align: left;\n", @@ -106,7 +69,7 @@ " display: block;\n", " white-space: wrap;\n", " text-align: left;\n", - " } \n", + " }\n", " h3 {\n", " font-family: 'Open sans',verdana,arial,sans-serif;\n", " }\n", @@ -149,13 +112,13 @@ " }\n", " div.output_subarea.output_text.output_pyout {\n", " overflow-x: auto;\n", - " overflow-y: scroll;\n", - " max-height: 50000px;\n", + " overflow-y: visible;\n", + " max-height: 5000000px;\n", " }\n", " div.output_subarea.output_stream.output_stdout.output_text {\n", " overflow-x: auto;\n", - " overflow-y: scroll;\n", - " max-height: 50000px;\n", + " overflow-y: visible;\n", + " max-height: 5000000px;\n", " }\n", " div.output_wrapper{\n", " margin-top:0.2em;\n", @@ -173,42 +136,38 @@ " margin: 2em;\n", " }\n", " ul li{\n", - " padding-left: 0.5em; \n", - " margin-bottom: 0.5em; \n", - " margin-top: 0.5em; \n", + " padding-left: 0.5em;\n", + " margin-bottom: 0.5em;\n", + " margin-top: 0.5em;\n", " }\n", " ul li li{\n", - " padding-left: 0.2em; \n", - " margin-bottom: 0.2em; \n", - " margin-top: 0.2em; \n", + " padding-left: 0.2em;\n", + " margin-bottom: 0.2em;\n", + " margin-top: 0.2em;\n", " }\n", " ol{\n", " margin: 2em;\n", " }\n", " ol li{\n", - " padding-left: 0.5em; \n", - " margin-bottom: 0.5em; \n", - " margin-top: 0.5em; \n", + " padding-left: 0.5em;\n", + " margin-bottom: 0.5em;\n", + " margin-top: 0.5em;\n", " }\n", " ul li{\n", - " padding-left: 0.5em; \n", - " margin-bottom: 0.5em; \n", - " margin-top: 0.2em; \n", + " padding-left: 0.5em;\n", + " margin-bottom: 0.5em;\n", + " margin-top: 0.2em;\n", " }\n", " a:link{\n", - " font-weight: bold;\n", " color:#447adb;\n", " }\n", " a:visited{\n", - " font-weight: bold;\n", " color: #1d3b84;\n", " }\n", " a:hover{\n", - " font-weight: bold;\n", " color: #1d3b84;\n", " }\n", " a:focus{\n", - " font-weight: bold;\n", " color:#447adb;\n", " }\n", " a:active{\n", @@ -216,10 +175,10 @@ " color:#447adb;\n", " }\n", " .rendered_html :link {\n", - " text-decoration: underline; \n", + " text-decoration: underline;\n", " }\n", " .rendered_html :hover {\n", - " text-decoration: none; \n", + " text-decoration: none;\n", " }\n", " .rendered_html :visited {\n", " text-decoration: none;\n", @@ -232,7 +191,7 @@ " }\n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", - " } \n", + " }\n", " hr {\n", " color: #f3f3f3;\n", " background-color: #f3f3f3;\n", @@ -266,11 +225,14 @@ " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", - " scale:95,\n", + " scale: 95,\n", " availableFonts: [],\n", " preferredFont:null,\n", " webFont: \"TeX\",\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", + " },\n", + " \"fast-preview\": {\n", + " \"disabled\": false\n", " }\n", " });\n", "\n" @@ -312,17 +274,15 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "50000\n", - "3.00621596517\n", - "1.99696435477\n" + "3.00067325106\n", + "2.00433280075\n" ] } ], @@ -352,14 +312,14 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false + "scrolled": true }, "outputs": [ { "data": { - "image/png": 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AUPq9wq0msjoWkxdIS0vDjh07AACBgYF48cUXDSciq3n99df18tKlS7F//36D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\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -392,15 +352,14 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -416,21 +375,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "that is not very useful to us - we want the PDF, not bin counts. Fortunately `hist` includes a `density` parameter which will plot the PDF for us." + "that is not very useful to us - we want the PDF, not bin counts. Fortunately `hist` includes a `normed` parameter which will plot the PDF for us." ] }, { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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HaZd8z+lIPs/fL4Chl15CUEgARftKSUvTZ01ycVRM4jPWpS3j5VdeAGDc5ZfS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\nYuOurxkQP5LaWheVleX4mQA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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -508,21 +461,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "There is another way to get the approximate distribution of a set of data. There is a technique called *kernel density estimate* that uses a kernel to estimate the probability distribution of a set of data. NumPy implements it with the function `gaussian_kde`. Do not be mislead by the name - Gaussian refers to the type of kernel used in the computation. This works for any distribution, not just Gaussians. In this section we have a Gaussian distribution, but soon we will not, and this same function will work." + "There is another way to get the approximate distribution of a set of data. There is a technique called *kernel density estimate* that uses a kernel to estimate the probability distribution of a set of data. SciPy implements it with the function `gaussian_kde`. Do not be mislead by the name - Gaussian refers to the type of kernel used in the computation. This works for any distribution, not just Gaussians. In this section we have a Gaussian distribution, but soon we will not, and this same function will work." ] }, { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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PHQDgmFX+zrV/hJDgUM1VEbmG/Tt+5kRlES611QMAwkMisCafN9QGopzUxYiL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\nXobBREREXoXBREREXoXBRER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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -552,15 +503,13 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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mXUycDJu2wf1Pwhup+WXNUke1tpjcvdTMbgMmERSyx9y90MxuCWb7WHf/t5ld\nYGafALuAG2KrdwJeMjOPbWucu+t698NUXLL/oGkXn3JdCEmSa3/JPuZ+Mi3sGBIhR3eBYf3g/YVh\nJ5HGlNA5JnefCPSrMu2RKuO3VbPeMmDI4QSUQ/vZjY/SukVbsix1Oz7ur6bQigD85ragNf3bZ5zv\nPxhyGGk06oyZgl6Z+iQA7fI70aZVh5DTSFVzFznf+j2UlsF0HbhOqt+Ph3Wbnad/prtlpLPU/Zid\nwfbG7iu3pWhDyEmS46ozbw07QlIV7YIpH6goNZSinbBnn27qms5UmFJQTlbQ0D1v5BUhJ0mOnOwm\nYUdImlemOGccdFAb9r4NR7Vv9Dhp4/Yr4cmfBsOvzYAWZ8LvxjvzFgePfftVqNKJClMK696pfhev\nRU2vzsfyk6/8HwDZ2Tns25+6dxov2nXwtKwsyG1S0ftQ6i4722iWV3na9x6AodcHjzWbwkglDUXn\nmFJMWVlptb3y0kVpaQkzC98sv1VRqurVGa46O3hWPUqOL5xuFD7tXH0XzE29b3qROlBhSjEvvfs3\nFq6aF3aMpMvKSq/G+0kD4b5bVJGSrV8P4z9/cr52fzD+j3eC50+3Q7dOTpMc7fN0kF7/DTLA7MK3\ny4eN9PkjbNe6E6cNvgCAV6Y9FXIaibK2rY0XfhE8jo5908vIm2Dah+HmkuRRYUoh23dtZfe+nQAM\nP+Y0Bvb6TMiJkqusrBQgZc8xTZ/v/Hpc2CkyS36LiuEzboPXpqsTRDpQYUohC1bOLR8+c/jnQ0zS\nMOILbSp+M+9b78GHS8JOkVnef9z4bNwl/N/+A7w7V8Up1akwpYB5n8xgwtQn+ff0p8un5TVpGmKi\nhtGv+/Hlw3f+5YaU/vLAC04OO0Hm+PN3oU/XYPiT1fDsG1C0U8UplanzQwqYv3QWMwsrvmXuM8ee\nTscU+xr1RFQ9Z1ZaWhJSksSNGees2wwlpbAx1sj73+vg6nPS5/xf1B3Xy/jl150r7gzGH/pH8Pt4\n+Afh5pL6U2FKAaVeWmm8uDQ9u4tnZ+dw9w1juftvXwNgxsdvcNJx59CqeX7IySrbt9/59dPw00N+\n5aU0psvPMO67xflx7A6em7bCvY8Hraa8JnBGv0OsLJGjwpSC+nQ5LuwIDaZt647lw69Oe4p+3QZH\nrzAVH7ooZesAeSh++GWjTSvnf34DL/03eAC0bgFn/CLcbFI3KkwppkXTVpx03Dlhx2hQvTsPYMna\nj8OOUa3cgnYVAAAKX0lEQVT5S53Bcd8w8rVLYUjf4NBRTjacdnxwaElE6k+FKcWcMmgUTXJyw47R\noL59xS/4zfjvs3LDYj5e/j49jjwm7EjVatUcHv6BilCUXHQKLFoFf3i2Ytr2XbB8Qx7dOuwLL5jU\niQpThG3ato4333uZggXvlE/LlPut7dkX3HTutZnjeW3meADOHnE5l4T0hYgr1jvjXof7/h6MH9MN\nPtR1wJHTtaPxvaudEwYE9y38euwLka/8xUDevH9OuOEkYToaHmGbi9Yzdf7r5eNXnXkrF550TYiJ\nGk+bVgffiru4ZB/ujd8NeOV65+6/wJ1jYU/sQ3eTHHT7m4jq3MG46mzj6rOhX/eK6ZfdM4iFK9SN\nPBWoMEXUpm3reGrSHytN69axd0hpGt+tl91Fn64DK017Z+6rbNu5ucG3/dTrzpMTg8eOXc7YCfD3\n1yrmd+sEP7upwWPIYWrVwih8xmgduzvE9t059L8GrrzTmbNIBSrKdCgvon417nb2l1QcE//axT+m\ne6c+ISZqXNlZ2Vw/6nuMf/Mh5i+d1ajbvvG+oDNDdX55K9zxJbWUUsmcx2HANWXsKw4+h7/wFuTm\nwDXnOu2PgJED9PuMGrWYImjO4mmVitJFJ12bdvfFS0TrFkfQvnWnStOemPh7NmxdE0qee25WUUpF\nR3c2fnvzJ5w2cFv5tKcnw0XfhwdfCDGY1EgtpgjZvW8nHy17j9dmPFNp+qDeJ4SUKHz9ew6jWV6L\n8g4QS9Z+zH1PfIPTjrmMozsMrGXtxKxc77w7Lzh/9N+5B7eWWjSD334TLvtsUjYnIRjZbwcj++1g\nwpzhvDIFdu6BJWvgqdeDQ7ddO8Ivvw6/Gw/D+kGzPLjhQhh6jD6IhEGFKSI2bl3DK9OeYt4n08OO\nEin9ewylf4+h5YXpgHmr3qVFXj4wol6vu2K9s31XcNPV34+H9xYevEyHI4JODs/dCycP0j+odHDP\nzcY9N8OTE52v/Lxi+uqNcN09wfCcRcHzkxPh2Xucc0/Q776xJVSYzGwU8AeCQ3+PufuvqlnmT8D5\nwC7genefm+i6maq0tITVm5YBsGbzsoOK0vknjOb8E0eHES1y8lu2o2jnlvLx7Xu2sHH7qnq91nNv\nOKN/euhlrjkHHr8TctTzLi2dfyI8f2/QqeXVqZCXC/uq3OmraCfc+hv4xuVO6+ZwwUmQlQVHttN7\noqHVWpjMLAt4EDgLWAvMNrN/uvuCuGXOB3q7e18zOwF4GDgxkXUz2UfL3+Mvr/7yoOmtmuXzo+se\noElOXgipoumH1/4R9zIemXAfy9cHzZv3V7zJ+398k9Fn/Q8nDzz3oHWWrnFmfgyrNsL/PgQnDICZ\nh7ihxD03w62fD74KvW1r/fNJZ+2PMC4/Ay4/o2Ja50uc9VsqL7dsLXzvgcrTxt0d9Oi76OSg558k\nXyItppHAYndfAWBm44FLgfjicinwBIC7zzSzfDPrBBydwLppb/e+naxYvxiAHbu3saVow0GHpg5o\nnteS74weQ4tmrRszYuQ1b9oSgGO6DSovTAClZdmMGTeVSTNHMvDoXRQs6MyXRq2kX3eY8G57Zhc2\nL1+2pqJ0VDu4+VK483r9k8lk7z4EpWXB8O+fhUdern65a+8Onk8aCDnZzvBj4dge0PNIdNgvSRIp\nTF2A+GMmqwmKVW3LdElw3ZT1/qIpvDD7EXbv38ETU+H8E6+mtLSYnXuKmL+0gBHHfpY33/9nwq+X\nm5PHRSd/iXZVeqJF2dI1zodLk/Na786DdZuDx9tz4PShwfMBJw+CprnX8uZ711a7fsGC4GavT03s\nXu18gOvOn0Z+izYUrpjDTRfnc2yP9hzf58Tk/ACS0np3rSgqo892+nWHNZtg7/6g8Hzzd5WXnz4/\neH53XsW0rCynLFbcvnE5/PlFOPE4WLoW2udD+yOCDjYH3tsXngyd2kJ+S9j4KXxuKAzsBWUOZWXB\nc+HilpS5sbXMK00/8Nw+H04ZnF4F0Wq7kt7MLgfOc/evxca/BIx092/FLfMK8Et3nxYb/w/wA4IW\n0yHXBSgqKtLVbiIiaSw/Pz/h6plIi2kNEP8RtGtsWtVlulWzTG4C64qIiJRL5ALb2UAfM+thZrnA\naGBClWUmAF8GMLMTgW3uviHBdUVERMrV2mJy91Izuw2YREWX70IzuyWY7WPd/d9mdoGZfULQXfyG\nQ61bdRt1aeKJiEh6q/Uck4iISGMK9V55ZvZFM5tvZqVmNqzKvB+a2WIzKzSzgy9SiRAzu8vMVpvZ\n+7HHqLAz1cbMRpnZAjNbZGZ3hJ2nLsxsuZnNM7M5Zta4d3itIzN7zMw2mNkHcdPamNkkM1toZq+b\nWbS+Oz6mhuwp8V43s65m9qaZfWRmH5rZt2LTI7/vq8n+zdj0yO97M8szs5mxv80Pzeyu2PQ67fdQ\nW0xm1g8oAx4Bvufu78em9weeBj5D0GHiP0Bfj2jzLrbzd7j772pdOAJiFz4vIu7CZ2B0qlz4bGZL\ngeHuvjXsLLUxs1OBncAT7j44Nu1XwBZ3HxP7UNDG3f83zJzVqSF7SrzXzexI4Eh3n2tmLYH3CK6h\nvIGI7/tDZL+K1Nj3zd19t5llA1OBbwGXU4f9HmqLyd0XuvtioOo5pkuB8e5e4u7LgcVE//qnVDpP\nVn7RtLsXAwcufE4VRorcGd/dpwBVC+ilQOy7cPk7cFmjhkpQDdkhBd7r7r7+wG3R3H0nUEjwITfy\n+76G7F1is1Nh3++ODeYR9GNw6rjfo/rHXfXC3DVU/GKi6jYzm2tmf4ni4YEqarogOlU4MNnMZpvZ\nzWGHqYeOsV6ruPt6oGPIeeoqld7rmFlPYAgwA+iUSvs+LvvM2KTI73szyzKzOcB6YLK7z6aO+73B\nC5OZTTazD+IeH8aeL27obSdTLT/HQ0Avdx9C8MuIdFM7DZzi7sOAC4BvxA45pbJIHqKuQUq912OH\nwl4Avh1rfVTd15Hd99VkT4l97+5l7j6UoIU60syOo477vcG/9sLdz6nHajVdsBuaOvwcjwKvNGSW\nJEjkounIcvd1sedNZvYSwaHJKeGmqpMNZtbJ3TfEzidsDDtQotx9U9xopN/rZpZD8I/9SXc/cG+w\nlNj31WVPpX0P4O7bzextYBR13O9ROpQXf+x0AjDazHLN7GigDxDZ3lexHX3AF4D5YWVJUMpe+Gxm\nzWOfJDGzFsC5RH9/Gwe/v6+PDX8FSPyGio2vUvYUe6//FfjY3f8YNy1V9v1B2VNh35tZ+wOHGM2s\nGXAOwTmyOu33sHvlXQY8ALQHtgFz3f382LwfAl8FigmaspNCC1oLM3uC4DhwGbAcuOXA8dSoinU1\n/SMVFz7fH3KkhMQ+qLxEcCggBxgX5exm9jRwOtAO2ADcBbwMPE9wVGAFcKW7b6vpNcJSQ/YzSIH3\nupmdAvwX+JDgveLAjwg+4D5HhPf9IbJfQ8T3vZkNIujckBV7POvu95lZW+qw33WBrYiIREqUDuWJ\niIioMImISLSoMImISKSoMImISKSoMImISKSoMImISKSoMImISKSoMImISKT8f6IxK8X9U8YEAAAA\nAElFTkSuQmCC\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -570,8 +519,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "mean = 14.00\n", - "std = 2.81\n" + "mean = 13.99\n", + "std = 2.80\n" ] } ], @@ -616,15 +565,13 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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DcMIe3XitvB+0zS+WxBQs0X6ckwEc9douxqg8/uj2tZs2bfLzpfuOHWKAzuOo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\nRU5ErkJggg==\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -634,7 +581,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "mean = -1.60\n", + "mean = -1.59\n", "std = 2.10\n" ] } @@ -671,9 +618,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -704,9 +649,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -734,15 +677,13 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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jmTvXvlLsGw6dHv5pUpUkXy9XhJVrlDLRunMLW3ndmg0cPXUK+vWzbSv+3Y/p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\nUF4MsG2ltQ5XSpXBSFCHtdZ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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -781,9 +722,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 }