read_xrdml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "cc200c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib_inline\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "import xrayutilities as xu\n",
    "import glob\n",
    "import os,re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "467510b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read files and sorted with time.\n",
    "file_list = sorted(glob.glob(\"*.xrdml\"),key=os.path.getmtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad96784f",
   "metadata": {},
   "outputs": [],
   "source": [
    "read_file = xu.io.XRDMLFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "659556b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Class method!\"\"\"\n",
    "\n",
    "class make_read_list_class(object):\n",
    "    def __init__(self,file_list):\n",
    "        name = self.__dict__\n",
    "        for i in range(len(file_list)):\n",
    "            file = read_file(file_list[i])\n",
    "            name['data_' + str(i)] = file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "569bc0d4",
   "metadata": {},
   "source": [
    "\"\"\"Globals & Locals method!\"\"\"\n",
    "\n",
    "def make_read_list(file_list):\n",
    "    \"\"\"From file_list read vars, and create \n",
    "        Var name is data[i].\n",
    "    \"\"\"\n",
    "    for f in range(len(file_list)):\n",
    "        createVar = globals()\n",
    "        createVar['data_' + str(f)] = read_file(file_list[f])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bdba36c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "All_data = make_read_list_class(file_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c2d2845",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_dict = All_data.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44349209",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_0\n",
      "data_1\n",
      "data_2\n",
      "data_3\n",
      "data_4\n"
     ]
    }
   ],
   "source": [
    "for dat in data_dict:\n",
    "    print(dat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89440e98",
   "metadata": {},
   "source": [
    "line = re.search(r'countTime with \\D\\d+',str(data_dict['data_3']))\n",
    "times = re.findall(r'\\d+',line[0])\n",
    "int(times[0])\n",
    "print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4e15617f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_times(data):\n",
    "    line_tmp = re.search(r'countTime with \\D\\d+',str(data_dict[data]))\n",
    "    times_tmp = re.findall(r'\\d+',line_tmp[0])\n",
    "    num = int(times_tmp[0])\n",
    "    return num"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb7da883",
   "metadata": {},
   "source": [
    "line_tmp = re.search(r'countTime with \\D\\d',str(data_dict['data_2']))\n",
    "times_tmp = re.findall(r'\\d',line_tmp[0])\n",
    "num = int(times_tmp[0])\n",
    "print(line_tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b1d17419",
   "metadata": {},
   "outputs": [],
   "source": [
    "class make_line_list_class(object):\n",
    "    def __init__(self,data_dict):\n",
    "        lines = self.__dict__\n",
    "        for d in data_dict:\n",
    "            line_name = f\"line_{d}\"\n",
    "            lines[line_name] =  find_times(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4c306e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'line_data_0': 1,\n",
       " 'line_data_1': 3,\n",
       " 'line_data_2': 10,\n",
       " 'line_data_3': 17,\n",
       " 'line_data_4': 21}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_line_list_class(data_dict).__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e2629c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_dict = make_line_list_class(data_dict).__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "25e6c2da",
   "metadata": {},
   "outputs": [],
   "source": [
    "class make_count_dict():\n",
    "    def __init__(self,data_dict):\n",
    "        counts = self.__dict__\n",
    "        for d in data_dict:\n",
    "            count = data_dict[d].scan.ddict['counts']\n",
    "            if count.ndim > 1:\n",
    "                counts['count_' + str(d)] = np.sum(count,axis=0)/find_times(d)\n",
    "            else:\n",
    "                counts['count_' + str(d)] = count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "adce1f1e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "count_averaged = make_count_dict(data_dict).__dict__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d48f0163",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_count = [value for value in count_averaged.values()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9f93ea95",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_count = np.concatenate(list_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "eab53118",
   "metadata": {},
   "outputs": [],
   "source": [
    "class make_theta_dict():\n",
    "    def __init__(self,data_dict):\n",
    "        thetas = self.__dict__\n",
    "        for t in data_dict:\n",
    "            theta = data_dict[t].scan.ddict['2Theta']\n",
    "            if theta.ndim > 1:\n",
    "                thetas['2theta_' + str(t)] = theta[0]\n",
    "            else:\n",
    "                thetas['2theta_' + str(t)] = theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7fa00572",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_dict = make_theta_dict(data_dict).__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fef1571e",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_theta = [value for value in theta_dict.values()]\n",
    "all_theta = np.concatenate(list_theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8341c01e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(all_theta,all_count)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "e4351c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = np.array((all_theta,all_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "7fbc9d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.01432394e+00, 2.04297182e+00, 2.07161970e+00, ...,\n",
       "        1.39929952e+02, 1.39958600e+02, 1.39987248e+02],\n",
       "       [7.97560000e+04, 7.83930000e+04, 7.79250000e+04, ...,\n",
       "        2.44909524e+03, 2.46033333e+03, 2.47785714e+03]])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "c40b4994",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"final_data.xy\",final_data,delimiter=' ')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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