added files for tutorial

fd8a9da0 · Astrid Muennich · 6e1cb761 · fd8a9da0 · fd8a9da0 · fd8a9da0
Commit fd8a9da0 authored 6 years ago by Astrid Muennich
--- a/docs/source/tutorial.rst
+++ b/docs/source/tutorial.rst
+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
--- a/notebooks/Tutorial/calversion.ipynb
+++ b/notebooks/Tutorial/calversion.ipynb
--- a/notebooks/Tutorial/startversion.ipynb
+++ b/notebooks/Tutorial/startversion.ipynb
+{
+ "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": "iVBORw0KGgoAAAANSUhEUgAAAVIAAADyCAYAAAAfvk21AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nawQAAAAASUVORK5CYII=\n",
+      "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
+}
+%% Cell type:markdown id: tags:
+Create a small simple notebook, without thinking about parameters or maxwell
+%% Cell type:code id: tags:
+``` python
+import matplotlib
+%matplotlib inline
+import numpy as np
+import matplotlib.pyplot as plt
+```
+%% Cell type:code id: tags:
+``` python
+sensor_size = [10,20]
+random_seed = 2345
+np.random.seed = random_seed
+fake_data = np.random.random((sensor_size))
+```
+%% Cell type:code id: tags:
+``` python
+plt.imshow(fake_data, interpolation="nearest")
+plt.colorbar()
+```
+%% Output
+    <matplotlib.colorbar.Colorbar at 0x7f9f38551d30>
+%% Cell type:markdown id: tags:
+Calculate some stuff from the data
+%% Cell type:code id: tags:
+``` python
+my_mean = fake_data.mean()
+my_std = fake_data.std()
+my_mean, my_std
+```
+%% Output
+    (0.5116986708989778, 0.29529469463486308)
+%% Cell type:code id: tags:
+``` python
+```