From fd8a9da015368cf8c134deb90f64cd2f76e51e50 Mon Sep 17 00:00:00 2001
From: Astrid Muennich <amunnich@max-exfl001.desy.de>
Date: Wed, 8 Aug 2018 18:02:23 +0200
Subject: [PATCH] added files for tutorial

---
 docs/source/tutorial.rst              |  33 ++++
 notebooks/Tutorial/calversion.ipynb   | 254 ++++++++++++++++++++++++++
 notebooks/Tutorial/startversion.ipynb | 134 ++++++++++++++
 3 files changed, 421 insertions(+)
 create mode 100644 docs/source/tutorial.rst
 create mode 100644 notebooks/Tutorial/calversion.ipynb
 create mode 100644 notebooks/Tutorial/startversion.ipynb

diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst
new file mode 100644
index 000000000..ecea4557c
--- /dev/null
+++ b/docs/source/tutorial.rst
@@ -0,0 +1,33 @@
+Installation and Configuration
+=============================
+1. log into max-exfl
+2. install karabo in your home directory or under /gpfs/exfel/data/scratch/username: 
+   wget http://exflserv05.desy.de/karabo/karaboFramework/tags/2.2.4/karabo-2.2.4-Release-CentOS-7-x86_64.sh
+   chmod +x karabo-2.2.4-Release-CentOS-7-x86_64.sh
+   ./karabo-2.2.4-Release-CentOS-7-x86_64.sh
+   source karabo/activate 
+3. get pycalibration
+   git clone https://git.xfel.eu/gitlab/detectors/pycalibration.git
+4. install requirements: 
+   cd pycalibration
+   pip install -r requirements.txt .
+5. adjust xfel_calibrate/settings.py:
+   change karabo_activate_path and ipcluster_path according to where you installed karabo
+
+Create your own notebook
+========================
+1. Create a new notebook or re-arrange an existing on following the guidelines explained here: https://in.xfel.eu/readthedocs/docs/european-xfel-offline-calibration/en/latest/workflow.html
+
+2. register you notebook:
+   add an entry to xfel_calibrate/notebooks.py
+   Note: use all capital letters for DETECTOR and TYPE
+3. update:
+   pip install --upgrade .
+
+
+Running the notebook
+====================
+1. make sure output folders exist
+2. run it: 
+   xfel-calibrate Tutorial TEST
+3. Look at generated report in output folder
diff --git a/notebooks/Tutorial/calversion.ipynb b/notebooks/Tutorial/calversion.ipynb
new file mode 100644
index 000000000..e59e77854
--- /dev/null
+++ b/notebooks/Tutorial/calversion.ipynb
@@ -0,0 +1,254 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial Calculation #\n",
+    "\n",
+    "Author: Astrid Muennich, Version 0.1\n",
+    "\n",
+    "A small example how to adapt a notebook to run with the offline calibration package \"pycalibation\".\n",
+    "\n",
+    "The first cell contains all parameters that should be exposed to the command line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "out_folder = \"/gpfs/exfel/data/scratch/amunnich/tutorial\" # output folder\n",
+    "sensor_size = [10, 30] # defining the picture size\n",
+    "random_seed = 2345 # random seed for filling of fake data array. Change it to produce different results.\n",
+    "runs = 500 # how may iterations to fill histograms\n",
+    "cluster_profile = \"tutorial\" "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First include what we need and set up the cluster profile. Everything that has a written response ina cell will show up in the report, e.g. prints but also return values or errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "No module named 'ipyparallel'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-75-f535e80204a3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mipyparallel\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mClient\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Connecting to profile {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcluster_profile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mImportError\u001b[0m: No module named 'ipyparallel'"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from ipyparallel import Client\n",
+    "\n",
+    "print(\"Connecting to profile {}\".format(cluster_profile))\n",
+    "view = Client(profile=cluster_profile)[:]\n",
+    "view.use_dill()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create some random data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def data_creation(random_seed):\n",
+    "    np.random.seed = random_seed\n",
+    "    return np.random.random((sensor_size))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fake_data = []\n",
+    "for i in range(runs):\n",
+    "    fake_data.append(data_creation(random_seed+10*i))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the random image. everything we write here in the markup cells will show up as text in the report."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YniQZPcRC9FeJS3MhhFCwMnVWIFkRoX/ainmdZNHTAysDJK1G0qoxSQNJrp61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\nCCFUVSSiEEIIVfX/AWPuhumfjEr5AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f426430d940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(211)\n",
+    "plt.imshow(fake_data[0], interpolation=\"nearest\")\n",
+    "plt.title('Random Image')\n",
+    "plt.ylabel('sensor height')\n",
+    "plt.subplot(212)\n",
+    "plt.imshow(fake_data[5], interpolation=\"nearest\")\n",
+    "plt.xlabel('sensor width')\n",
+    "plt.ylabel('sensor height')\n",
+    "plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)\n",
+    "cax = plt.axes([0.85, 0.1, 0.075, 0.9])\n",
+    "plt.colorbar(cax=cax).ax.set_ylabel(\"# counts\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These plots show two randomly filled sensor images. We can use markup cells also as captions for images."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mean = []\n",
+    "std = []\n",
+    "for im in fake_data:\n",
+    "    mean.append(im.mean())\n",
+    "    std.append(im.std())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We calculate the mean value of all images, as well as the standard deviation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEZCAYAAACTsIJzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmBgkSS0mBklSy/8HZ6oU/qFH3FkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4264087320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(mean, 50)\n",
+    "plt.xlabel('mean')\n",
+    "plt.ylabel('counts')\n",
+    "plt.title('Mean value')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f42640ac7b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(std, 50)\n",
+    "plt.xlabel('mean')\n",
+    "plt.ylabel('counts')\n",
+    "plt.title('Mean value')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/Tutorial/startversion.ipynb b/notebooks/Tutorial/startversion.ipynb
new file mode 100644
index 000000000..f9a61dfd4
--- /dev/null
+++ b/notebooks/Tutorial/startversion.ipynb
@@ -0,0 +1,134 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a small simple notebook, without thinking about parameters or maxwell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sensor_size = [10,20]\n",
+    "random_seed = 2345\n",
+    "np.random.seed = random_seed\n",
+    "fake_data = np.random.random((sensor_size))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7f9f38551d30>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f9f3873cc18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(fake_data, interpolation=\"nearest\")\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate some stuff from the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.5116986708989778, 0.29529469463486308)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_mean = fake_data.mean()\n",
+    "my_std = fake_data.std()\n",
+    "my_mean, my_std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
-- 
GitLab