adding file structure and jupyter demo notebook

demo/demo_short.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Demo Short\n",
+    "## Short demo script demonstrating basic functionality of INSIGHT-SingleImage.\n",
+    "For a more complex example see the `demo.py` file.\n",
+    "\n",
+    "We start defining the paths, loading the geometry parameters, preprocess a lambda image a bit\n",
+    "do the reshaping process including intensity corrections and do some exemplary plots and cuts.\n",
+    "Then we will optimize the sdd by fitting a given reflex q position and do more plotting."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### import python modules, and the *INSIGHT* package"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "##############################\n",
+      "##### Welcome to INSIGHT #####\n",
+      "##############################\n",
+      "\n",
+      "No version could be determined.\n",
+      "Set plot style: insight\n",
+      "Set plotting backend to:\n",
+      "TkAgg\n",
+      "\n",
+      "##############################\n",
+      "##### Start of script ########\n",
+      "##############################\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import glob\n",
+    "import os\n",
+    "\n",
+    "# import the INSIGHT package as ins\n",
+    "import insight_scatter as ins"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:15:40.227817Z",
+     "end_time": "2023-04-19T15:15:42.087141Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## A general remark for help option\n",
+    "Type `help(OBJECT)` to get more information about the corresponding class.\n",
+    "E.g. for the *Params* class you could type into the console `help(MyParams)`.\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Download a test detector image"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File exists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gdown\n",
+    "\n",
+    "url_lambda = 'https://drive.google.com/file/d/11LvS5UmImslth9Qm458_MihVJezMXwEf/view?usp=sharing'\n",
+    "if not os.path.exists('data'):\n",
+    "    os.mkdir('data')\n",
+    "output = 'data/lambda4.cbf'\n",
+    "if not os.path.isfile(output):\n",
+    "    gdown.download(url_lambda, output, fuzzy=True)\n",
+    "else:\n",
+    "    print(\"File exists.\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "\n",
+    "\n",
+    "## Load lambda or pilatus files\n",
+    "specify folder-path to the images"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "folderpath = os.path.abspath(r'data')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:15:48.160543Z",
+     "end_time": "2023-04-19T15:15:48.176544Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "specify identifier for your image, this can be the exact name or part of name(s) using the wildcard symbol *\n",
+    "here we import all files that contain the name lambda and end in .cbf"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "img_identifier = 'lambda*.cbf'"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:15:49.957520Z",
+     "end_time": "2023-04-19T15:15:49.973290Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "now `folderpath` and `img_identifier` are used to load all images that match the requirements\n",
+    "all those image paths are stored in `raw_path_list`"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [],
+   "source": [
+    "raw_path_list = ins.sort_natural(glob.glob(os.path.join(folderpath, img_identifier)))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:15:52.348915Z",
+     "end_time": "2023-04-19T15:15:52.367945Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "let's check if all images are loaded correctly ( there should be a none-empty list)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['C:\\\\Users\\\\ge46bak\\\\PycharmProjects\\\\INSIGHT\\\\insight-scatter\\\\demo\\\\data\\\\lambda_01.cbf', 'C:\\\\Users\\\\ge46bak\\\\PycharmProjects\\\\INSIGHT\\\\insight-scatter\\\\demo\\\\data\\\\lambda_02.cbf', 'C:\\\\Users\\\\ge46bak\\\\PycharmProjects\\\\INSIGHT\\\\insight-scatter\\\\demo\\\\data\\\\lambda_03.cbf', 'C:\\\\Users\\\\ge46bak\\\\PycharmProjects\\\\INSIGHT\\\\insight-scatter\\\\demo\\\\data\\\\lambda_04.cbf']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(raw_path_list)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:15:53.993573Z",
+     "end_time": "2023-04-19T15:15:54.013832Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Load reshape parameters\n",
+    "to load all necessary parameters from a txt file we use the insight-scatter.Params class\n",
+    "the text-file containing all needed parameters is already included in this demo\n",
+    "check out the demo file by navigating to the demo/parameters folder and open the txt file\n",
+    "this file is loaded using the Params class using the init function\n",
+    "we have to give the filepath as argument\n",
+    "os.path.join will join the path of the current directory with our subfolder and txt file name"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imported the following static parameters from\n",
+      "C:\\Users\\ge46bak\\PycharmProjects\\INSIGHT\\insight-scatter\\demo\\parameters\\params_Lambda9M.txt:\n",
+      "db_x = 5.0\n",
+      "db_y = 1578.5\n",
+      "sdd = 240.0\n",
+      "inca = 0.4\n",
+      "px_size = 0.055\n",
+      "wl = 1.0507\n",
+      "det_rot_x = 0.9669255716443013\n",
+      "det_rot_y = 0.0\n",
+      "det_rot_z = 0.0\n",
+      "air_attenuation_coeff = 0.0011839\n",
+      "si_attenuation_coeff = 2.33\n",
+      "det_thickness = 0.3\n",
+      "horizontal_polarization_fraction = 0.98\n",
+      "det_rot_x was calculated from provided specular beam coordinates.\n"
+     ]
+    }
+   ],
+   "source": [
+    "MyParams = ins.Params(filepath=os.path.join(os.getcwd(), r'parameters/params_Lambda9M.txt'))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:15:56.461002Z",
+     "end_time": "2023-04-19T15:15:56.505685Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "As you can see, the output shows some errors - go check the parameter input file and correct the wrong lines. In this\n",
+    "case the program can continue, but if you ignore such errors, the code might fail later!\n",
+    "You can always check the loaded parameters by typing `MyParams.params_dict` into the console or `MyParams.print_dict()`"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'db_x': 5.0,\n 'db_y': 1578.5,\n 'sdd': 240.0,\n 'inca': 0.4,\n 'px_size': 0.055,\n 'wl': 1.0507,\n 'det_rot_x': 0.9669255716443013,\n 'det_rot_y': 0.0,\n 'det_rot_z': 0.0,\n 'air_attenuation_coeff': 0.0011839,\n 'si_attenuation_coeff': 2.33,\n 'det_thickness': 0.3,\n 'horizontal_polarization_fraction': 0.98}"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MyParams.get_params()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:15:58.194020Z",
+     "end_time": "2023-04-19T15:15:58.215022Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Load and plot raw image\n",
+    "Now we load the image, for this we use the central class of INSIGHT: the SingleImage class\n",
+    "This class will store the image and call other classes that might manipulate the image\n",
+    "use the init function to load the image\n",
+    "here we load the first (and only) image from the list of files in raw_path_list\n",
+    "We also specify the set of parameters for this image."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:fabio.cbfimage:Defaulting type to int32\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Loaded image C:\\Users\\ge46bak\\PycharmProjects\\INSIGHT\\insight-scatter\\demo\\data\\lambda_01.cbf\n"
+     ]
+    }
+   ],
+   "source": [
+    "MySingleImage = ins.SingleImage(MyParams, raw_path_list[0])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:16:00.531726Z",
+     "end_time": "2023-04-19T15:16:00.663117Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "To check the image we can plot it\n",
+    "If you need to rotate, flip etc your image check out the commands in the SingleImage class documentation\n",
+    "or via help(MySingleImage)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [],
+   "source": [
+    "MySingleImage.plot_raw_image(pretty=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:16:02.328170Z",
+     "end_time": "2023-04-19T15:16:03.369474Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Before we further proceed we might want to delete hot pixels\n",
+    "For this we set a maximum intensity of allowed values"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [],
+   "source": [
+    "MySingleImage.delete_hot_pixels(max_int=2e4)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:16:04.194586Z",
+     "end_time": "2023-04-19T15:16:04.261091Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "which you should control afterwards (not to delete good data as well!) by"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [],
+   "source": [
+    "MySingleImage.plot_raw_image(pretty=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:16:07.922606Z",
+     "end_time": "2023-04-19T15:16:08.913272Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Now we are set up and can get to the real stuff.\n",
+    "Let's transform the image to q-space vie the calculate_geometry() function.\n",
+    "No further input is required, since SingleImage already got the image and the corresponding parameters."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Geometry] boost=2 performed in 0.5 s!\n"
+     ]
+    }
+   ],
+   "source": [
+    "MySingleImage.calculate_geometry()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:16:09.640581Z",
+     "end_time": "2023-04-19T15:16:10.145007Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "And now we can continue to calculate various corrections to the intensity values.\n",
+    "For more details check out the documentation.\n",
+    "This class handles all intensity corrections: angular sensitivity, solid angle, air attenuation,\n",
+    "polarization correction and a user defined arbitrary correction map."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Intensity] boost=2 performed in 0.16 s!\n"
+     ]
+    }
+   ],
+   "source": [
+    "MySingleImage.calculate_corrected_intensity()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:16:12.575295Z",
+     "end_time": "2023-04-19T15:16:12.794238Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We can check the result by plotting the reshaped image"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [],
+   "source": [
+    "MySingleImage.plot_reshaped_image(pretty=True, show=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:16:30.194490Z",
+     "end_time": "2023-04-19T15:16:31.418206Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "To create a WAXS cut use the create_cut_waxs function and provide a name and the cut limits\n",
+    "to look up more details about a function hit Str+B to jump to the source code of the function at your cursor"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating MAPI with q_min = 0.96, q_max = 1.04, chi_min = -10, chi_max = 90\n"
+     ]
+    }
+   ],
+   "source": [
+    "MySingleImage.create_cut_waxs('MAPI', q_min=0.96, q_max=1.04, chi_min=-10, chi_max=90)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:16:41.768110Z",
+     "end_time": "2023-04-19T15:16:42.363430Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "the following line to see the raw values of each pixel sorted along `q` in blue.\n",
+    "We can also bin the data and add it to the plot in yellos."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "outputs": [],
+   "source": [
+    "MySingleImage.MAPI.plot_raw(along='q', show=True)\n",
+    "MySingleImage.MAPI.plot_binned(along='q', show=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:17:09.044021Z",
+     "end_time": "2023-04-19T15:17:09.434526Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "You can use this peak to optimize or correct the sdd by giving reference value\n",
+    "the cut should only contain the reference Bragg peak - so make it quite narrow"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using binned data for sdd optimization fit.\n",
+      "[[Model]]\n",
+      "    (Model(gaussian, prefix='g1_') + Model(constant, prefix='c1_'))\n",
+      "[[Fit Statistics]]\n",
+      "    # fitting method   = least_squares\n",
+      "    # function evals   = 85\n",
+      "    # data points      = 1000\n",
+      "    # variables        = 4\n",
+      "    chi-square         = 31456498.7\n",
+      "    reduced chi-square = 31582.8300\n",
+      "    Akaike info crit   = 10364.3609\n",
+      "    Bayesian info crit = 10383.9919\n",
+      "    R-squared          = 0.97544076\n",
+      "[[Variables]]\n",
+      "    g1_amplitude:  42.9443062 +/- 0.28115310 (0.65%) (init = 10)\n",
+      "    g1_center:     0.98370979 +/- 2.7111e-05 (0.00%) (init = 1.008)\n",
+      "    g1_sigma:      0.00422982 +/- 2.8824e-05 (0.68%) (init = 0.01)\n",
+      "    c1_c:          237.294388 +/- 6.62849585 (2.79%) (init = 10)\n",
+      "    g1_fwhm:       0.00996047 +/- 6.7875e-05 (0.68%) == '2.3548200*g1_sigma'\n",
+      "    g1_height:     4050.35974 +/- 22.9657174 (0.57%) == '0.3989423*g1_amplitude/max(1e-15, g1_sigma)'\n",
+      "[[Correlations]] (unreported correlations are < 0.100)\n",
+      "    C(g1_amplitude, g1_sigma) = +0.6405\n",
+      "    C(g1_amplitude, c1_c)     = -0.5303\n",
+      "    C(g1_sigma, c1_c)         = -0.3396\n",
+      "[[Model]]\n",
+      "    (Model(gaussian, prefix='g1_') + Model(constant, prefix='c1_'))\n",
+      "[[Fit Statistics]]\n",
+      "    # fitting method   = least_squares\n",
+      "    # function evals   = 85\n",
+      "    # data points      = 1000\n",
+      "    # variables        = 4\n",
+      "    chi-square         = 31456498.7\n",
+      "    reduced chi-square = 31582.8300\n",
+      "    Akaike info crit   = 10364.3609\n",
+      "    Bayesian info crit = 10383.9919\n",
+      "    R-squared          = 0.97544076\n",
+      "[[Variables]]\n",
+      "    g1_amplitude:  42.9443062 +/- 0.28115310 (0.65%) (init = 10)\n",
+      "    g1_center:     0.98370979 +/- 2.7111e-05 (0.00%) (init = 1.008)\n",
+      "    g1_sigma:      0.00422982 +/- 2.8824e-05 (0.68%) (init = 0.01)\n",
+      "    c1_c:          237.294388 +/- 6.62849585 (2.79%) (init = 10)\n",
+      "    g1_fwhm:       0.00996047 +/- 6.7875e-05 (0.68%) == '2.3548200*g1_sigma'\n",
+      "    g1_height:     4050.35974 +/- 22.9657174 (0.57%) == '0.3989423*g1_amplitude/max(1e-15, g1_sigma)'\n",
+      "[[Correlations]] (unreported correlations are < 0.100)\n",
+      "    C(g1_amplitude, g1_sigma) = +0.6405\n",
+      "    C(g1_amplitude, c1_c)     = -0.5303\n",
+      "    C(g1_sigma, c1_c)         = -0.3396\n",
+      "\n",
+      "Reshaping with new sdd=234.17673603092985 mm. Old was sdd = 240.0 mm.\n",
+      "[Geometry] boost=2 performed in 0.46 s!\n",
+      "[Intensity] boost=2 performed in 0.17 s!\n",
+      "Updating all cuts.\n",
+      "Creating MAPI with q_min = 0.96, q_max = 1.04, chi_min = -10, chi_max = 90\n",
+      "The desired name of your cut MAPI existed already - overwriting!\n"
+     ]
+    }
+   ],
+   "source": [
+    "MySingleImage.optimize_sdd('MAPI', 1.008, plot=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:17:21.172128Z",
+     "end_time": "2023-04-19T15:17:22.740523Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The SDD changed - let's see if the peak position now matches better to the reference value `1.008`"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [],
+   "source": [
+    "MySingleImage.MAPI.plot_raw(along='q', show=True)\n",
+    "MySingleImage.MAPI.plot_binned(along='q', show=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:17:41.388337Z",
+     "end_time": "2023-04-19T15:17:41.821376Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Let's now do a pseudo XRD cut of the complete image"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating psXRD with q_min = 0, q_max = 5, chi_min = -10, chi_max = 90\n",
+      "The desired name of your cut psXRD existed already - overwriting!\n"
+     ]
+    }
+   ],
+   "source": [
+    "MySingleImage.create_cut_waxs('psXRD', 0, 5, -10, 90)\n",
+    "MySingleImage.psXRD.plot_binned(along='q', show=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:17:49.498170Z",
+     "end_time": "2023-04-19T15:17:51.941467Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Now we want to make an azimuthal tube cut"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating MAPItube with q_min = 0.96, q_max = 1.04, chi_min = -10, chi_max = 90\n"
+     ]
+    }
+   ],
+   "source": [
+    "MySingleImage.create_cut_waxs('MAPItube', 0.96, 1.04, -10, 90)\n",
+    "MySingleImage.MAPItube.plot_binned(along='chi', show=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:18:01.509559Z",
+     "end_time": "2023-04-19T15:18:02.302095Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "To visualize the cuts we can draw them on the reshaped image\n",
+    "if you closed it just copy and past the line above into the console to plot the image again.\n",
+    "You can do this also with the other cuts by providing the corresponding name"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "outputs": [],
+   "source": [
+    "MySingleImage.plot_reshaped_image(pretty=True, show=True)\n",
+    "MySingleImage.plot_cut_outline_waxs('MAPItube', color='red')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-19T15:18:14.049795Z",
+     "end_time": "2023-04-19T15:18:15.773584Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Thank you for working you way through this tutorial. This is the same content as in `demo_short.py`, so now it is time\n",
+    "for you to move to the other demo."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file

demo/parameters/params_Lambda9M.txt

+# direct beam coordinates in pixel
+db_x = 5
+db_y = 1578.5
+
+# specular beam position in pixel
+spec_x = 3
+spec_y = 1697
+
+# sample-to-detector distance in mm
+sdd = 240
+
+# incidence angle in degree
+inca = 0.4
+
+# detector pixel size in mm
+px_size = 0.055
+
+# wavelength in Angstrom
+wl = 1.0507
+
+# detector rotation in degree (x along primary beam direction)
+#det_rot_x = -0.6368657183977084
+det_rot_y = 0.0
+det_rot_z = 0.0
+
+# air attenuation coefficient in
+air_attenuation_coeff = 0.0011839
+
+# silicon attenuation coefficient in
+si_attenuation_coeff = 2.33
+
+# detector thickness in mm
+det_thickness = 0.3
+
+# horizontal polarization fraction between 0 and 1, use 'None' for no correction
+horizontal_polarization_fraction = 0.98
\ No newline at end of file