Extract_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract data from .sol files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This workbook can be used to extract physical paramaters from a folder a .sol files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from src.cmwp_tools import extractDataDir\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract data from solution files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code extracts data from CMWP .sol files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsing 68 solution files...\n",
      "Reading /home/rhys/Documents/CMWP-210315/DESY_202107/sam1a/y_-0.166_z_12.621.sol                     \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rhys/anaconda3/lib/python3.7/site-packages/scipy/optimize/optimize.py:283: RuntimeWarning: Values in x were outside bounds during a minimize step, clipping to bounds\n",
      "  \"minimize step, clipping to bounds\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsed 67 solution files succesfully                                                                    \n",
      "\"*** END\" not present in 1                                                                     \n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "data_df = extractDataDir('/home/rhys/Documents/CMWP-210315/DESY_202107/sam1a/*.sol', ellipticity=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
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       "          a1    a1_neg    a1_pos        a2    a2_neg    a2_pos     a_err  \\\n",
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       "1   0.992272  0.815648  0.796794 -0.277112  0.077868  0.055700  0.000078   \n",
       "2   0.799896  0.311160  0.186376 -0.291739  0.030633  0.040552  0.000080   \n",
       "3   0.193147  0.343802  0.504114 -0.163725  0.060742  0.057631  0.000062   \n",
       "4   0.066070  0.369990  0.358098 -0.175534  0.075129  0.054767  0.000078   \n",
       "..       ...       ...       ...       ...       ...       ...       ...   \n",
       "62  0.025838  0.284216  0.475416 -0.155129  0.050107  0.058018  0.000059   \n",
       "63 -0.287704  0.268428  0.397032 -0.162512  0.044528  0.048429  0.000065   \n",
       "64 -0.607793  0.314229  0.281408 -0.149121  0.051596  0.061587  0.000057   \n",
       "65 -0.064812  0.261192  0.289062 -0.142447  0.058261  0.071651  0.000076   \n",
       "66 -0.294183  0.350078  0.400089 -0.086366  0.074447  0.083861  0.000064   \n",
       "\n",
       "       a_lat    a_loop  a_loop_neg  ...     d_pos          e      e_neg  \\\n",
       "0   3.231346  0.999982    0.000002  ...  4.928991   0.038286   0.002190   \n",
       "1   3.231451  0.716658    0.073860  ...  0.847659   0.098829   0.013935   \n",
       "2   3.232650  0.757518    0.059519  ...  0.227759   0.122637   0.002428   \n",
       "3   3.231670  0.628376    0.073929  ...  0.028290   6.248680   0.359924   \n",
       "4   3.230737  0.655637    0.074948  ...  0.097101   2.901030   1.148808   \n",
       "..       ...       ...         ...  ...       ...        ...        ...   \n",
       "62  3.230640  0.629840    0.076370  ...  0.045408  31.956800  18.215376   \n",
       "63  3.230758  0.668690    0.071813  ...  0.099593   4.336420   2.202901   \n",
       "64  3.230292  0.679405    0.095964  ...  0.087798   2.252680   0.732121   \n",
       "65  3.230221  0.619320    0.092569  ...  0.051678  19.761500   8.596253   \n",
       "66  3.229947  0.559844    0.100268  ...  0.063702   7.576450   4.644364   \n",
       "\n",
       "       e_pos               filename  \\\n",
       "0   0.020368  y_-0.166_z_12.619.sol   \n",
       "1   0.053862  y_-0.166_z_12.621.sol   \n",
       "2   0.031763  y_-0.166_z_12.623.sol   \n",
       "3   3.143086  y_-0.166_z_12.625.sol   \n",
       "4   0.248908  y_-0.166_z_12.627.sol   \n",
       "..       ...                    ...   \n",
       "62  3.515248   y_0.500_z_12.643.sol   \n",
       "63  0.490015   y_0.500_z_12.647.sol   \n",
       "64  0.380703   y_0.500_z_12.651.sol   \n",
       "65  9.762181   y_0.500_z_12.655.sol   \n",
       "66  0.728854   y_0.500_z_12.659.sol   \n",
       "\n",
       "                                             peak_int  \\\n",
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       "4   [2206480.0, 10132800.0, 9787390.0, 2736060.0, ...   \n",
       "..                                                ...   \n",
       "62  [2279770.0, 7825980.0, 9758240.0, 3160450.0, 2...   \n",
       "63  [1851170.0, 8610240.0, 9726420.0, 3216430.0, 2...   \n",
       "64  [2395850.0, 9673080.0, 9910280.0, 2825610.0, 1...   \n",
       "65  [2777620.0, 10553700.0, 9383920.0, 2821380.0, ...   \n",
       "66  [2350220.0, 8135120.000000001, 9237120.0, 2984...   \n",
       "\n",
       "                                           peak_names  \\\n",
       "0   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "1   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "2   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "3   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "4   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "..                                                ...   \n",
       "62  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "63  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "64  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "65  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "66  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "\n",
       "                                             peak_pos  rhoIGS       wssr  \n",
       "0   [3.76225, 4.08966, 4.28103, 5.55673, 6.51654, ...     0.0   144265.0  \n",
       "1   [3.76187, 4.08906, 4.28141, 5.55636, 6.51704, ...     0.0   256652.0  \n",
       "2   [3.76071, 4.09053, 4.28043, 5.5562, 6.51455, 7...     0.0  1593670.0  \n",
       "3   [3.76124, 4.08892, 4.28123, 5.55594, 6.51678, ...     0.0  7085190.0  \n",
       "4   [3.76288, 4.08849, 4.28194, 5.55628, 6.51817, ...     0.0  7116650.0  \n",
       "..                                                ...     ...        ...  \n",
       "62  [3.76333, 4.08729, 4.2823, 5.55586, 6.51814, 7...     0.0  5275760.0  \n",
       "63  [3.76365, 4.08747, 4.28223, 5.55616, 6.51878, ...     0.0  6736990.0  \n",
       "64  [3.76312, 4.08738, 4.28212, 5.55631, 6.51879, ...     0.0  8029930.0  \n",
       "65  [3.76223, 4.08774, 4.28192, 5.55613, 6.51818, ...     0.0  7220310.0  \n",
       "66  [3.76335, 4.08733, 4.28243, 5.55617, 6.51904, ...     0.0  6209710.0  \n",
       "\n",
       "[67 rows x 34 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print dataframe\n",
    "data_df\n",
    "#data_df.style.set_sticky(axis=\"filename\") #need pandas 1.3.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pull out z and y values from filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df[['f2', 'y', 'f4', 'zs']] = data_df['filename'].str.split('_',expand=True)               # Split by '_'\n",
    "data_df[['z', 'no']] = data_df['zs'].str.split('.s',expand=True)                                # Split by '.s'\n",
    "data_df.drop(['f2', 'filename', 'f4', 'zs', 'no'], axis=1, inplace=True)                        # Clear un-needed columns\n",
    "data_df['y'] = pd.to_numeric(data_df['y'], downcast=\"float\")\n",
    "data_df['z'] = pd.to_numeric(data_df['z'], downcast=\"float\")\n",
    "\n",
    "# Rearrange to put y and z first\n",
    "cols = data_df.columns.tolist()\n",
    "data_df=data_df[cols[-2:] + cols[:-2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>a1</th>\n",
       "      <th>a1_neg</th>\n",
       "      <th>a1_pos</th>\n",
       "      <th>a2</th>\n",
       "      <th>a2_neg</th>\n",
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       "      <th>a_err</th>\n",
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       "      <th>...</th>\n",
       "      <th>d_new_pos</th>\n",
       "      <th>d_pos</th>\n",
       "      <th>e</th>\n",
       "      <th>e_neg</th>\n",
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       "      <th>peak_int</th>\n",
       "      <th>peak_names</th>\n",
       "      <th>peak_pos</th>\n",
       "      <th>rhoIGS</th>\n",
       "      <th>wssr</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.166</td>\n",
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       "      <td>0.000094</td>\n",
       "      <td>3.231346</td>\n",
       "      <td>...</td>\n",
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       "      <td>4.928991</td>\n",
       "      <td>0.038286</td>\n",
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       "      <td>[123098.0, 423303.0, 514660.0, 70488.1, 78501....</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76225, 4.08966, 4.28103, 5.55673, 6.51654, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144265.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.166</td>\n",
       "      <td>12.621</td>\n",
       "      <td>0.992272</td>\n",
       "      <td>0.815648</td>\n",
       "      <td>0.796794</td>\n",
       "      <td>-0.277112</td>\n",
       "      <td>0.077868</td>\n",
       "      <td>0.055700</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>3.231451</td>\n",
       "      <td>...</td>\n",
       "      <td>1.272636</td>\n",
       "      <td>0.847659</td>\n",
       "      <td>0.098829</td>\n",
       "      <td>0.013935</td>\n",
       "      <td>0.053862</td>\n",
       "      <td>[213066.0, 957625.0, 909591.0, 149038.0, 13181...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76187, 4.08906, 4.28141, 5.55636, 6.51704, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>256652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.166</td>\n",
       "      <td>12.623</td>\n",
       "      <td>0.799896</td>\n",
       "      <td>0.311160</td>\n",
       "      <td>0.186376</td>\n",
       "      <td>-0.291739</td>\n",
       "      <td>0.030633</td>\n",
       "      <td>0.040552</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>3.232650</td>\n",
       "      <td>...</td>\n",
       "      <td>0.322930</td>\n",
       "      <td>0.227759</td>\n",
       "      <td>0.122637</td>\n",
       "      <td>0.002428</td>\n",
       "      <td>0.031763</td>\n",
       "      <td>[1241230.0, 4953110.0, 4664190.0, 782739.0, 71...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76071, 4.09053, 4.28043, 5.5562, 6.51455, 7...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1593670.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.166</td>\n",
       "      <td>12.625</td>\n",
       "      <td>0.193147</td>\n",
       "      <td>0.343802</td>\n",
       "      <td>0.504114</td>\n",
       "      <td>-0.163725</td>\n",
       "      <td>0.060742</td>\n",
       "      <td>0.057631</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>3.231670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040716</td>\n",
       "      <td>0.028290</td>\n",
       "      <td>6.248680</td>\n",
       "      <td>0.359924</td>\n",
       "      <td>3.143086</td>\n",
       "      <td>[3720770.0, 12215300.0, 14594000.0, 3152450.0,...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76124, 4.08892, 4.28123, 5.55594, 6.51678, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7085190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.166</td>\n",
       "      <td>12.627</td>\n",
       "      <td>0.066070</td>\n",
       "      <td>0.369990</td>\n",
       "      <td>0.358098</td>\n",
       "      <td>-0.175534</td>\n",
       "      <td>0.075129</td>\n",
       "      <td>0.054767</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>3.230737</td>\n",
       "      <td>...</td>\n",
       "      <td>0.134052</td>\n",
       "      <td>0.097101</td>\n",
       "      <td>2.901030</td>\n",
       "      <td>1.148808</td>\n",
       "      <td>0.248908</td>\n",
       "      <td>[2206480.0, 10132800.0, 9787390.0, 2736060.0, ...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76288, 4.08849, 4.28194, 5.55628, 6.51817, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7116650.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.500</td>\n",
       "      <td>12.643</td>\n",
       "      <td>0.025838</td>\n",
       "      <td>0.284216</td>\n",
       "      <td>0.475416</td>\n",
       "      <td>-0.155129</td>\n",
       "      <td>0.050107</td>\n",
       "      <td>0.058018</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>3.230640</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063636</td>\n",
       "      <td>0.045408</td>\n",
       "      <td>31.956800</td>\n",
       "      <td>18.215376</td>\n",
       "      <td>3.515248</td>\n",
       "      <td>[2279770.0, 7825980.0, 9758240.0, 3160450.0, 2...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76333, 4.08729, 4.2823, 5.55586, 6.51814, 7...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5275760.0</td>\n",
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       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.500</td>\n",
       "      <td>12.647</td>\n",
       "      <td>-0.287704</td>\n",
       "      <td>0.268428</td>\n",
       "      <td>0.397032</td>\n",
       "      <td>-0.162512</td>\n",
       "      <td>0.044528</td>\n",
       "      <td>0.048429</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>3.230758</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128702</td>\n",
       "      <td>0.099593</td>\n",
       "      <td>4.336420</td>\n",
       "      <td>2.202901</td>\n",
       "      <td>0.490015</td>\n",
       "      <td>[1851170.0, 8610240.0, 9726420.0, 3216430.0, 2...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76365, 4.08747, 4.28223, 5.55616, 6.51878, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6736990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.500</td>\n",
       "      <td>12.651</td>\n",
       "      <td>-0.607793</td>\n",
       "      <td>0.314229</td>\n",
       "      <td>0.281408</td>\n",
       "      <td>-0.149121</td>\n",
       "      <td>0.051596</td>\n",
       "      <td>0.061587</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>3.230292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106678</td>\n",
       "      <td>0.087798</td>\n",
       "      <td>2.252680</td>\n",
       "      <td>0.732121</td>\n",
       "      <td>0.380703</td>\n",
       "      <td>[2395850.0, 9673080.0, 9910280.0, 2825610.0, 1...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76312, 4.08738, 4.28212, 5.55631, 6.51879, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8029930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.500</td>\n",
       "      <td>12.655</td>\n",
       "      <td>-0.064812</td>\n",
       "      <td>0.261192</td>\n",
       "      <td>0.289062</td>\n",
       "      <td>-0.142447</td>\n",
       "      <td>0.058261</td>\n",
       "      <td>0.071651</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>3.230221</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072045</td>\n",
       "      <td>0.051678</td>\n",
       "      <td>19.761500</td>\n",
       "      <td>8.596253</td>\n",
       "      <td>9.762181</td>\n",
       "      <td>[2777620.0, 10553700.0, 9383920.0, 2821380.0, ...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76223, 4.08774, 4.28192, 5.55613, 6.51818, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7220310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.500</td>\n",
       "      <td>12.659</td>\n",
       "      <td>-0.294183</td>\n",
       "      <td>0.350078</td>\n",
       "      <td>0.400089</td>\n",
       "      <td>-0.086366</td>\n",
       "      <td>0.074447</td>\n",
       "      <td>0.083861</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>3.229947</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090122</td>\n",
       "      <td>0.063702</td>\n",
       "      <td>7.576450</td>\n",
       "      <td>4.644364</td>\n",
       "      <td>0.728854</td>\n",
       "      <td>[2350220.0, 8135120.000000001, 9237120.0, 2984...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76335, 4.08733, 4.28243, 5.55617, 6.51904, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6209710.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        y       z        a1    a1_neg    a1_pos        a2    a2_neg    a2_pos  \\\n",
       "0  -0.166  12.619  1.327630  0.734179  0.439446 -0.669552  0.153997  0.180779   \n",
       "1  -0.166  12.621  0.992272  0.815648  0.796794 -0.277112  0.077868  0.055700   \n",
       "2  -0.166  12.623  0.799896  0.311160  0.186376 -0.291739  0.030633  0.040552   \n",
       "3  -0.166  12.625  0.193147  0.343802  0.504114 -0.163725  0.060742  0.057631   \n",
       "4  -0.166  12.627  0.066070  0.369990  0.358098 -0.175534  0.075129  0.054767   \n",
       "..    ...     ...       ...       ...       ...       ...       ...       ...   \n",
       "62  0.500  12.643  0.025838  0.284216  0.475416 -0.155129  0.050107  0.058018   \n",
       "63  0.500  12.647 -0.287704  0.268428  0.397032 -0.162512  0.044528  0.048429   \n",
       "64  0.500  12.651 -0.607793  0.314229  0.281408 -0.149121  0.051596  0.061587   \n",
       "65  0.500  12.655 -0.064812  0.261192  0.289062 -0.142447  0.058261  0.071651   \n",
       "66  0.500  12.659 -0.294183  0.350078  0.400089 -0.086366  0.074447  0.083861   \n",
       "\n",
       "       a_err     a_lat  ...  d_new_pos     d_pos          e      e_neg  \\\n",
       "0   0.000094  3.231346  ...   5.974714  4.928991   0.038286   0.002190   \n",
       "1   0.000078  3.231451  ...   1.272636  0.847659   0.098829   0.013935   \n",
       "2   0.000080  3.232650  ...   0.322930  0.227759   0.122637   0.002428   \n",
       "3   0.000062  3.231670  ...   0.040716  0.028290   6.248680   0.359924   \n",
       "4   0.000078  3.230737  ...   0.134052  0.097101   2.901030   1.148808   \n",
       "..       ...       ...  ...        ...       ...        ...        ...   \n",
       "62  0.000059  3.230640  ...   0.063636  0.045408  31.956800  18.215376   \n",
       "63  0.000065  3.230758  ...   0.128702  0.099593   4.336420   2.202901   \n",
       "64  0.000057  3.230292  ...   0.106678  0.087798   2.252680   0.732121   \n",
       "65  0.000076  3.230221  ...   0.072045  0.051678  19.761500   8.596253   \n",
       "66  0.000064  3.229947  ...   0.090122  0.063702   7.576450   4.644364   \n",
       "\n",
       "       e_pos                                           peak_int  \\\n",
       "0   0.020368  [123098.0, 423303.0, 514660.0, 70488.1, 78501....   \n",
       "1   0.053862  [213066.0, 957625.0, 909591.0, 149038.0, 13181...   \n",
       "2   0.031763  [1241230.0, 4953110.0, 4664190.0, 782739.0, 71...   \n",
       "3   3.143086  [3720770.0, 12215300.0, 14594000.0, 3152450.0,...   \n",
       "4   0.248908  [2206480.0, 10132800.0, 9787390.0, 2736060.0, ...   \n",
       "..       ...                                                ...   \n",
       "62  3.515248  [2279770.0, 7825980.0, 9758240.0, 3160450.0, 2...   \n",
       "63  0.490015  [1851170.0, 8610240.0, 9726420.0, 3216430.0, 2...   \n",
       "64  0.380703  [2395850.0, 9673080.0, 9910280.0, 2825610.0, 1...   \n",
       "65  9.762181  [2777620.0, 10553700.0, 9383920.0, 2821380.0, ...   \n",
       "66  0.728854  [2350220.0, 8135120.000000001, 9237120.0, 2984...   \n",
       "\n",
       "                                           peak_names  \\\n",
       "0   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "1   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "2   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "3   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "4   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "..                                                ...   \n",
       "62  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "63  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "64  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "65  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "66  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "\n",
       "                                             peak_pos  rhoIGS       wssr  \n",
       "0   [3.76225, 4.08966, 4.28103, 5.55673, 6.51654, ...     0.0   144265.0  \n",
       "1   [3.76187, 4.08906, 4.28141, 5.55636, 6.51704, ...     0.0   256652.0  \n",
       "2   [3.76071, 4.09053, 4.28043, 5.5562, 6.51455, 7...     0.0  1593670.0  \n",
       "3   [3.76124, 4.08892, 4.28123, 5.55594, 6.51678, ...     0.0  7085190.0  \n",
       "4   [3.76288, 4.08849, 4.28194, 5.55628, 6.51817, ...     0.0  7116650.0  \n",
       "..                                                ...     ...        ...  \n",
       "62  [3.76333, 4.08729, 4.2823, 5.55586, 6.51814, 7...     0.0  5275760.0  \n",
       "63  [3.76365, 4.08747, 4.28223, 5.55616, 6.51878, ...     0.0  6736990.0  \n",
       "64  [3.76312, 4.08738, 4.28212, 5.55631, 6.51879, ...     0.0  8029930.0  \n",
       "65  [3.76223, 4.08774, 4.28192, 5.55613, 6.51818, ...     0.0  7220310.0  \n",
       "66  [3.76335, 4.08733, 4.28243, 5.55617, 6.51904, ...     0.0  6209710.0  \n",
       "\n",
       "[67 rows x 35 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print dataframe\n",
    "data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate depth from edge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This takes a list of all y positions (calculated from the data list above) \n",
    "and a list of the corresponsing z positions at the edge of the sample, \n",
    "to make a new column called 'depth', which is the distance of the pattern away from the sample edge in microns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.166 -0.5    0.167  0.5  ]\n"
     ]
    }
   ],
   "source": [
    "# These are the unique y positions\n",
    "data_df['y'] = pd.to_numeric(data_df['y'], downcast=\"float\")\n",
    "unique = data_df['y'].unique()\n",
    "print(unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12.623, 12.623, 12.623, 12.621]\n",
      "[12.623, 12.623, 12.623, 12.621]\n"
     ]
    }
   ],
   "source": [
    "##########################################\n",
    "# Here, define the edge (z value) for each y\n",
    "##########################################\n",
    "\n",
    "edges = [12.623, 12.623, 12.623, 12.621]\n",
    "print(edges)\n",
    "\n",
    "if len(unique) != len(edges):\n",
    "    raise Exception('Unique and edges should be the same length')\n",
    "print(edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df.insert(0, 'depth', data_df['y'])\n",
    "data_df['depth'].replace(to_replace=unique, value=edges, inplace=True)\n",
    "data_df['depth'] = data_df['z'] - data_df['depth']\n",
    "data_df['depth'] = np.round(data_df['depth'] * 1000,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depth</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>a1</th>\n",
       "      <th>a1_neg</th>\n",
       "      <th>a1_pos</th>\n",
       "      <th>a2</th>\n",
       "      <th>a2_neg</th>\n",
       "      <th>a2_pos</th>\n",
       "      <th>a_err</th>\n",
       "      <th>...</th>\n",
       "      <th>d_new_pos</th>\n",
       "      <th>d_pos</th>\n",
       "      <th>e</th>\n",
       "      <th>e_neg</th>\n",
       "      <th>e_pos</th>\n",
       "      <th>peak_int</th>\n",
       "      <th>peak_names</th>\n",
       "      <th>peak_pos</th>\n",
       "      <th>rhoIGS</th>\n",
       "      <th>wssr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.0</td>\n",
       "      <td>-0.166</td>\n",
       "      <td>12.619</td>\n",
       "      <td>1.327630</td>\n",
       "      <td>0.734179</td>\n",
       "      <td>0.439446</td>\n",
       "      <td>-0.669552</td>\n",
       "      <td>0.153997</td>\n",
       "      <td>0.180779</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>...</td>\n",
       "      <td>5.974714</td>\n",
       "      <td>4.928991</td>\n",
       "      <td>0.038286</td>\n",
       "      <td>0.002190</td>\n",
       "      <td>0.020368</td>\n",
       "      <td>[123098.0, 423303.0, 514660.0, 70488.1, 78501....</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76225, 4.08966, 4.28103, 5.55673, 6.51654, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.0</td>\n",
       "      <td>-0.166</td>\n",
       "      <td>12.621</td>\n",
       "      <td>0.992272</td>\n",
       "      <td>0.815648</td>\n",
       "      <td>0.796794</td>\n",
       "      <td>-0.277112</td>\n",
       "      <td>0.077868</td>\n",
       "      <td>0.055700</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>...</td>\n",
       "      <td>1.272636</td>\n",
       "      <td>0.847659</td>\n",
       "      <td>0.098829</td>\n",
       "      <td>0.013935</td>\n",
       "      <td>0.053862</td>\n",
       "      <td>[213066.0, 957625.0, 909591.0, 149038.0, 13181...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76187, 4.08906, 4.28141, 5.55636, 6.51704, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>256652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.166</td>\n",
       "      <td>12.623</td>\n",
       "      <td>0.799896</td>\n",
       "      <td>0.311160</td>\n",
       "      <td>0.186376</td>\n",
       "      <td>-0.291739</td>\n",
       "      <td>0.030633</td>\n",
       "      <td>0.040552</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>...</td>\n",
       "      <td>0.322930</td>\n",
       "      <td>0.227759</td>\n",
       "      <td>0.122637</td>\n",
       "      <td>0.002428</td>\n",
       "      <td>0.031763</td>\n",
       "      <td>[1241230.0, 4953110.0, 4664190.0, 782739.0, 71...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76071, 4.09053, 4.28043, 5.5562, 6.51455, 7...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1593670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.166</td>\n",
       "      <td>12.625</td>\n",
       "      <td>0.193147</td>\n",
       "      <td>0.343802</td>\n",
       "      <td>0.504114</td>\n",
       "      <td>-0.163725</td>\n",
       "      <td>0.060742</td>\n",
       "      <td>0.057631</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040716</td>\n",
       "      <td>0.028290</td>\n",
       "      <td>6.248680</td>\n",
       "      <td>0.359924</td>\n",
       "      <td>3.143086</td>\n",
       "      <td>[3720770.0, 12215300.0, 14594000.0, 3152450.0,...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76124, 4.08892, 4.28123, 5.55594, 6.51678, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7085190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.166</td>\n",
       "      <td>12.627</td>\n",
       "      <td>0.066070</td>\n",
       "      <td>0.369990</td>\n",
       "      <td>0.358098</td>\n",
       "      <td>-0.175534</td>\n",
       "      <td>0.075129</td>\n",
       "      <td>0.054767</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>...</td>\n",
       "      <td>0.134052</td>\n",
       "      <td>0.097101</td>\n",
       "      <td>2.901030</td>\n",
       "      <td>1.148808</td>\n",
       "      <td>0.248908</td>\n",
       "      <td>[2206480.0, 10132800.0, 9787390.0, 2736060.0, ...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76288, 4.08849, 4.28194, 5.55628, 6.51817, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7116650.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>12.643</td>\n",
       "      <td>0.025838</td>\n",
       "      <td>0.284216</td>\n",
       "      <td>0.475416</td>\n",
       "      <td>-0.155129</td>\n",
       "      <td>0.050107</td>\n",
       "      <td>0.058018</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063636</td>\n",
       "      <td>0.045408</td>\n",
       "      <td>31.956800</td>\n",
       "      <td>18.215376</td>\n",
       "      <td>3.515248</td>\n",
       "      <td>[2279770.0, 7825980.0, 9758240.0, 3160450.0, 2...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76333, 4.08729, 4.2823, 5.55586, 6.51814, 7...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5275760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>12.647</td>\n",
       "      <td>-0.287704</td>\n",
       "      <td>0.268428</td>\n",
       "      <td>0.397032</td>\n",
       "      <td>-0.162512</td>\n",
       "      <td>0.044528</td>\n",
       "      <td>0.048429</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128702</td>\n",
       "      <td>0.099593</td>\n",
       "      <td>4.336420</td>\n",
       "      <td>2.202901</td>\n",
       "      <td>0.490015</td>\n",
       "      <td>[1851170.0, 8610240.0, 9726420.0, 3216430.0, 2...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76365, 4.08747, 4.28223, 5.55616, 6.51878, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6736990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>12.651</td>\n",
       "      <td>-0.607793</td>\n",
       "      <td>0.314229</td>\n",
       "      <td>0.281408</td>\n",
       "      <td>-0.149121</td>\n",
       "      <td>0.051596</td>\n",
       "      <td>0.061587</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106678</td>\n",
       "      <td>0.087798</td>\n",
       "      <td>2.252680</td>\n",
       "      <td>0.732121</td>\n",
       "      <td>0.380703</td>\n",
       "      <td>[2395850.0, 9673080.0, 9910280.0, 2825610.0, 1...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76312, 4.08738, 4.28212, 5.55631, 6.51879, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8029930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>12.655</td>\n",
       "      <td>-0.064812</td>\n",
       "      <td>0.261192</td>\n",
       "      <td>0.289062</td>\n",
       "      <td>-0.142447</td>\n",
       "      <td>0.058261</td>\n",
       "      <td>0.071651</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072045</td>\n",
       "      <td>0.051678</td>\n",
       "      <td>19.761500</td>\n",
       "      <td>8.596253</td>\n",
       "      <td>9.762181</td>\n",
       "      <td>[2777620.0, 10553700.0, 9383920.0, 2821380.0, ...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76223, 4.08774, 4.28192, 5.55613, 6.51818, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7220310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>38.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>12.659</td>\n",
       "      <td>-0.294183</td>\n",
       "      <td>0.350078</td>\n",
       "      <td>0.400089</td>\n",
       "      <td>-0.086366</td>\n",
       "      <td>0.074447</td>\n",
       "      <td>0.083861</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090122</td>\n",
       "      <td>0.063702</td>\n",
       "      <td>7.576450</td>\n",
       "      <td>4.644364</td>\n",
       "      <td>0.728854</td>\n",
       "      <td>[2350220.0, 8135120.000000001, 9237120.0, 2984...</td>\n",
       "      <td>[100, 002, 101, 102, 110, 013, 020, 112, 021, ...</td>\n",
       "      <td>[3.76335, 4.08733, 4.28243, 5.55617, 6.51904, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6209710.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    depth      y       z        a1    a1_neg    a1_pos        a2    a2_neg  \\\n",
       "0    -4.0 -0.166  12.619  1.327630  0.734179  0.439446 -0.669552  0.153997   \n",
       "1    -2.0 -0.166  12.621  0.992272  0.815648  0.796794 -0.277112  0.077868   \n",
       "2     0.0 -0.166  12.623  0.799896  0.311160  0.186376 -0.291739  0.030633   \n",
       "3     2.0 -0.166  12.625  0.193147  0.343802  0.504114 -0.163725  0.060742   \n",
       "4     4.0 -0.166  12.627  0.066070  0.369990  0.358098 -0.175534  0.075129   \n",
       "..    ...    ...     ...       ...       ...       ...       ...       ...   \n",
       "62   22.0  0.500  12.643  0.025838  0.284216  0.475416 -0.155129  0.050107   \n",
       "63   26.0  0.500  12.647 -0.287704  0.268428  0.397032 -0.162512  0.044528   \n",
       "64   30.0  0.500  12.651 -0.607793  0.314229  0.281408 -0.149121  0.051596   \n",
       "65   34.0  0.500  12.655 -0.064812  0.261192  0.289062 -0.142447  0.058261   \n",
       "66   38.0  0.500  12.659 -0.294183  0.350078  0.400089 -0.086366  0.074447   \n",
       "\n",
       "      a2_pos     a_err  ...  d_new_pos     d_pos          e      e_neg  \\\n",
       "0   0.180779  0.000094  ...   5.974714  4.928991   0.038286   0.002190   \n",
       "1   0.055700  0.000078  ...   1.272636  0.847659   0.098829   0.013935   \n",
       "2   0.040552  0.000080  ...   0.322930  0.227759   0.122637   0.002428   \n",
       "3   0.057631  0.000062  ...   0.040716  0.028290   6.248680   0.359924   \n",
       "4   0.054767  0.000078  ...   0.134052  0.097101   2.901030   1.148808   \n",
       "..       ...       ...  ...        ...       ...        ...        ...   \n",
       "62  0.058018  0.000059  ...   0.063636  0.045408  31.956800  18.215376   \n",
       "63  0.048429  0.000065  ...   0.128702  0.099593   4.336420   2.202901   \n",
       "64  0.061587  0.000057  ...   0.106678  0.087798   2.252680   0.732121   \n",
       "65  0.071651  0.000076  ...   0.072045  0.051678  19.761500   8.596253   \n",
       "66  0.083861  0.000064  ...   0.090122  0.063702   7.576450   4.644364   \n",
       "\n",
       "       e_pos                                           peak_int  \\\n",
       "0   0.020368  [123098.0, 423303.0, 514660.0, 70488.1, 78501....   \n",
       "1   0.053862  [213066.0, 957625.0, 909591.0, 149038.0, 13181...   \n",
       "2   0.031763  [1241230.0, 4953110.0, 4664190.0, 782739.0, 71...   \n",
       "3   3.143086  [3720770.0, 12215300.0, 14594000.0, 3152450.0,...   \n",
       "4   0.248908  [2206480.0, 10132800.0, 9787390.0, 2736060.0, ...   \n",
       "..       ...                                                ...   \n",
       "62  3.515248  [2279770.0, 7825980.0, 9758240.0, 3160450.0, 2...   \n",
       "63  0.490015  [1851170.0, 8610240.0, 9726420.0, 3216430.0, 2...   \n",
       "64  0.380703  [2395850.0, 9673080.0, 9910280.0, 2825610.0, 1...   \n",
       "65  9.762181  [2777620.0, 10553700.0, 9383920.0, 2821380.0, ...   \n",
       "66  0.728854  [2350220.0, 8135120.000000001, 9237120.0, 2984...   \n",
       "\n",
       "                                           peak_names  \\\n",
       "0   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "1   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "2   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "3   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "4   [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "..                                                ...   \n",
       "62  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "63  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "64  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "65  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "66  [100, 002, 101, 102, 110, 013, 020, 112, 021, ...   \n",
       "\n",
       "                                             peak_pos  rhoIGS       wssr  \n",
       "0   [3.76225, 4.08966, 4.28103, 5.55673, 6.51654, ...     0.0   144265.0  \n",
       "1   [3.76187, 4.08906, 4.28141, 5.55636, 6.51704, ...     0.0   256652.0  \n",
       "2   [3.76071, 4.09053, 4.28043, 5.5562, 6.51455, 7...     0.0  1593670.0  \n",
       "3   [3.76124, 4.08892, 4.28123, 5.55594, 6.51678, ...     0.0  7085190.0  \n",
       "4   [3.76288, 4.08849, 4.28194, 5.55628, 6.51817, ...     0.0  7116650.0  \n",
       "..                                                ...     ...        ...  \n",
       "62  [3.76333, 4.08729, 4.2823, 5.55586, 6.51814, 7...     0.0  5275760.0  \n",
       "63  [3.76365, 4.08747, 4.28223, 5.55616, 6.51878, ...     0.0  6736990.0  \n",
       "64  [3.76312, 4.08738, 4.28212, 5.55631, 6.51879, ...     0.0  8029930.0  \n",
       "65  [3.76223, 4.08774, 4.28192, 5.55613, 6.51818, ...     0.0  7220310.0  \n",
       "66  [3.76335, 4.08733, 4.28243, 5.55617, 6.51904, ...     0.0  6209710.0  \n",
       "\n",
       "[67 rows x 36 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plots dislocation density, <a> loop fraction, M paramater and lattice paramaters as a function of depth from sample edge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46.490374681311735, 46.652461958621686)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "depth, dpa = np.loadtxt('data/srim.txt')\n",
    "\n",
    "fig, ((ax, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3, figsize=(20,9))\n",
    "fig.subplots_adjust(left=0.06, right=0.96, top=0.98, bottom=0.1, hspace=0.3, wspace = 0.35)\n",
    "axes = [ax, ax2, ax3, ax4, ax5, ax6]\n",
    "\n",
    "max_x = np.max(data_df['depth'])\n",
    "x=data_df['depth']; a=data_df['a_lat']; c=data_df['c_lat']; a_err=data_df['a_err']; c_err=data_df['c_err'];\n",
    "\n",
    "# Common settings for all axes\n",
    "for sax in axes:\n",
    "    sax.set_xlabel('Depth [${\\mu}m$]', fontsize=14)\n",
    "    sax.tick_params(axis='both', which='major', labelsize=12)\n",
    "    sax.set_xlim(0, max_x)\n",
    "    #sax.vlines(line_x, ymin=0, ymax=300, linestyles='dashed', alpha=0.3)\n",
    "    sax.xaxis.set_ticks_position('both')\n",
    "    \n",
    "    sec = sax.twinx()\n",
    "    sec.fill(depth, np.array(dpa), alpha=0.2)\n",
    "    sec.set_xlim(0, max_x)\n",
    "    sec.set_ylim(0)\n",
    "    sec.set_ylabel('Dose [dpa]', fontsize=14)\n",
    "    \n",
    "    sec.tick_params(axis='both', which='major', labelsize=12)\n",
    "\n",
    "#### dislocation density\n",
    "    \n",
    "ax.errorbar(x=x, y=data_df['d_new'], yerr = [data_df['d_new_neg'],data_df['d_new_pos']],  marker='o',c='k',  ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "ax.set_ylabel('Dislocation density [$10^{14} m^{-2}$]', fontsize=14)\n",
    "ax.set_zorder(sec.get_zorder()+1); ax.patch.set_visible(False);\n",
    "ax.set_ylim(0)\n",
    "\n",
    "#### m\n",
    "\n",
    "ax2.errorbar(x=x, y=np.exp(2)*data_df['e'], yerr = np.exp(2)*np.array([data_df['e_neg'],data_df['e_pos']]), marker='o',c='k',  ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "ax2.set_ylabel('M', fontsize=14)\n",
    "ax2.set_ylim(0, 10)\n",
    "ax2.set_zorder(sec.get_zorder()+1); ax2.patch.set_visible(False);\n",
    "\n",
    "#### a loop fraction\n",
    "\n",
    "ax3.errorbar(x=x, y=data_df['a_loop'], yerr = [data_df['a_loop_neg'],data_df['a_loop_pos']],  marker='o',c='k',  ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "ax3.set_ylabel('$f_{<a>}$', fontsize=14)\n",
    "ax3.set_ylim(0, 1)\n",
    "ax3.set_zorder(sec.get_zorder()+1); ax3.patch.set_visible(False);\n",
    "\n",
    "lim_defs=[np.round(np.min(a)-0.001,3), np.round(np.max(a)+0.001,3), np.round(np.min(c)-0.001,3), np.round(np.max(c)+0.001,3)]\n",
    "ratio_a_to_c = (np.max(a)-np.min(a)+0.002)/((np.max(a)-np.min(a)+0.002)+(np.max(c)-np.min(c))+0.002)\n",
    "\n",
    "#### c paramater\n",
    "\n",
    "ax4.errorbar(x=x, y=c, yerr=c_err, marker='o', c='k', ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "ax4.set_ylabel('c [Å]', fontsize=14)\n",
    "ax4.set_ylim(lim_defs[2], lim_defs[3])\n",
    "ax4.set_zorder(sec.get_zorder()+1); ax4.patch.set_visible(False);\n",
    "\n",
    "pos1 = ax4.get_position() # get the original position \n",
    "\n",
    "pos2 = [pos1.x0, pos1.y0,  pos1.width, pos1.height*0.5] \n",
    "ax4.set_position(pos2)\n",
    "ax4.xaxis.set_ticks_position('bottom')\n",
    "ax4.set_yticks(np.arange(lim_defs[2], lim_defs[3]-0.001, 0.001))\n",
    "\n",
    "#### a paramater\n",
    "\n",
    "pos2 = [pos1.x0, pos1.y0 + pos1.height *0.5,  pos1.width, pos1.height*0.5] \n",
    "ax4b = plt.axes(position=pos2)\n",
    "ax4b.errorbar(x=x, y=a, yerr=a_err, marker='o',c='k',  ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "ax4b.set_ylabel('a [Å]', fontsize=14)\n",
    "ax4b.tick_params(axis='both', which='major', labelsize=12)\n",
    "ax4b.xaxis.set_ticks_position('top')\n",
    "ax4b.xaxis.set_ticklabels([])\n",
    "ax4b.set_xlim(0, max_x)\n",
    "ax4b.set_ylim(lim_defs[0], lim_defs[1])\n",
    "ax4b.set_yticks(np.arange(lim_defs[0], lim_defs[1]+0.001, 0.001))\n",
    "ax4b.set_zorder(sec.get_zorder()+1); ax.patch.set_visible(False);\n",
    "\n",
    "#### c/a\n",
    "\n",
    "covera = np.array(c)/np.array(a)\n",
    "ax5.errorbar(x=x, y=covera, yerr=covera*np.sqrt((np.array(c_err)/np.array(c))**2+(np.array(a_err)/np.array(a))**2), marker='o',c='k',  ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "ax5.set_ylabel('c/a', fontsize=14)\n",
    "ax5.set_ylim(np.min(np.array(c)/np.array(a))-0.0003, np.max(np.array(c)/np.array(a))+0.0003)\n",
    "ax5.set_zorder(sec.get_zorder()+1); ax5.patch.set_visible(False);\n",
    "\n",
    "#### cell volume\n",
    "vol = np.array(a)**2*np.sin(np.deg2rad(60))*np.array(c)\n",
    "\n",
    "err_vol = vol * np.sqrt((np.array(c_err)/np.array(c))**2+2*(np.array(a_err)/np.array(a))**2)\n",
    "\n",
    "ax6.errorbar(x=x, y=vol, yerr=err_vol, c='k', marker='o', ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "ax6.set_ylabel('Cell volume [$Å^3$]', fontsize=14)\n",
    "ax6.set_zorder(sec.get_zorder()+1); ax6.patch.set_visible(False);\n",
    "ax6.set_ylim(np.min(vol)-0.05, np.max(vol)+0.05)\n",
    "\n",
    "#plt.savefig('/home/rhys/Dropbox (Research Group)/Postdoc/DESY/'+name+'_ellip'+str(ellip)+'.pdf', dpi=600)\n",
    "#plt.show()"
   ]
  },
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": []
  }
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