day2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bfe81af2-1978-4e1a-814d-8e6082c60ad7",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">Nov 30<sup>th</sup> 2021</div>\n",
    "\n",
    "# Programming Tutorial, Day 2\n",
    "Part of the *Better With Scattering* Workshop by www.bam.de"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eee5517-60aa-47a8-a6c9-2a15d72ad3ad",
   "metadata": {},
   "source": [
    "## Working with files and folders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04bbc6bc-a49c-4ab0-8e9b-542c9bd66d01",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Let Python show us the current working directory this script is running from.\n",
    "The output depends on where you stored the notebook file and it will be different for everyone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "543bc76d-bd3a-4daa-ad05-f1edb2cdd6f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/Users/ingo/code/jupyter_tutorial_sas')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "cwd = Path().absolute() # get current working directory\n",
    "cwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a0b27cfa-ad6a-4038-90eb-8699b2889862",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PosixPath('/Users/ingo/code'), 'jupyter_tutorial_sas')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cwd.parent, cwd.name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962addbb-0eba-45e8-bcbb-c926eb5ee190",
   "metadata": {},
   "source": [
    "### List the contents (files and folders) of the current working directory.\n",
    "If you put the notebook in an empty folder, this notebook file should be listed only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0eec377-7b33-4101-8ad5-122ecfac2917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/Users/ingo/code/jupyter_tutorial_sas/.DS_Store'),\n",
       " PosixPath('/Users/ingo/code/jupyter_tutorial_sas/LICENSE'),\n",
       " PosixPath('/Users/ingo/code/jupyter_tutorial_sas/day1 (live colab session).ipynb'),\n",
       " PosixPath('/Users/ingo/code/jupyter_tutorial_sas/day2.ipynb'),\n",
       " PosixPath('/Users/ingo/code/jupyter_tutorial_sas/Programming Tutorial.pptx'),\n",
       " PosixPath('/Users/ingo/code/jupyter_tutorial_sas/day2 (live colab session).ipynb'),\n",
       " PosixPath('/Users/ingo/code/jupyter_tutorial_sas/.gitignore'),\n",
       " PosixPath('/Users/ingo/code/jupyter_tutorial_sas/.ipynb_checkpoints'),\n",
       " PosixPath('/Users/ingo/code/jupyter_tutorial_sas/day3.ipynb'),\n",
       " PosixPath('/Users/ingo/code/jupyter_tutorial_sas/.git'),\n",
       " PosixPath('/Users/ingo/code/jupyter_tutorial_sas/day1.ipynb'),\n",
       " PosixPath('/Users/ingo/code/jupyter_tutorial_sas/data')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(cwd.iterdir()) # list contents of current directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8793dd45-1463-40d4-84af-6dff05b54b95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.DS_Store',\n",
       " 'LICENSE',\n",
       " 'day1 (live colab session).ipynb',\n",
       " 'day2.ipynb',\n",
       " 'Programming Tutorial.pptx',\n",
       " 'day2 (live colab session).ipynb',\n",
       " '.gitignore',\n",
       " '.ipynb_checkpoints',\n",
       " 'day3.ipynb',\n",
       " '.git',\n",
       " 'day1.ipynb',\n",
       " 'data']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[fn.name for fn in cwd.iterdir()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d31b8ef-a2d9-4890-ab69-0298fb315d9f",
   "metadata": {},
   "source": [
    "## Filtering files\n",
    "For automated file processing ;)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5525f359-6134-4f1f-b408-521a86328244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Programming Tutorial.pptx']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[fn.name for fn in cwd.iterdir() if fn.name.endswith(\".pptx\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75a2231-f2aa-4a9e-9b38-5f9700d38f98",
   "metadata": {},
   "source": [
    "### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d709178-ad63-433b-9dfd-8225a20b5868",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('data/Ag_N008.dat')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fnData = [fn for fn in Path(\"data\").iterdir() if fn.name.endswith(\".dat\")]\n",
    "fnData[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "64d6f1f8-66e5-4047-93d1-02ad0605bc49",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>q</th>\n",
       "      <th>I</th>\n",
       "      <th>uI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.030259</td>\n",
       "      <td>4769.301045</td>\n",
       "      <td>1655.363275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.030719</td>\n",
       "      <td>5346.031551</td>\n",
       "      <td>2218.324041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.031096</td>\n",
       "      <td>6387.856371</td>\n",
       "      <td>1534.356131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.031611</td>\n",
       "      <td>2986.364914</td>\n",
       "      <td>1984.290104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.032103</td>\n",
       "      <td>5634.075856</td>\n",
       "      <td>1367.733213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>14.165907</td>\n",
       "      <td>0.036509</td>\n",
       "      <td>0.024799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>14.334260</td>\n",
       "      <td>0.019789</td>\n",
       "      <td>0.024856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>14.504613</td>\n",
       "      <td>0.036952</td>\n",
       "      <td>0.024934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>14.676991</td>\n",
       "      <td>0.053573</td>\n",
       "      <td>0.025039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>14.851418</td>\n",
       "      <td>0.008345</td>\n",
       "      <td>0.025058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>535 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             q            I           uI\n",
       "0     0.030259  4769.301045  1655.363275\n",
       "1     0.030719  5346.031551  2218.324041\n",
       "2     0.031096  6387.856371  1534.356131\n",
       "3     0.031611  2986.364914  1984.290104\n",
       "4     0.032103  5634.075856  1367.733213\n",
       "..         ...          ...          ...\n",
       "530  14.165907     0.036509     0.024799\n",
       "531  14.334260     0.019789     0.024856\n",
       "532  14.504613     0.036952     0.024934\n",
       "533  14.676991     0.053573     0.025039\n",
       "534  14.851418     0.008345     0.025058\n",
       "\n",
       "[535 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "dataframe = pandas.read_csv(fnData[0], sep=r'\\s+', names=(\"q\", \"I\", \"uI\"))\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb9102bd-8eb8-4c62-89f5-b76ceed45514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      4769.301045\n",
       "1      5346.031551\n",
       "2      6387.856371\n",
       "3      2986.364914\n",
       "4      5634.075856\n",
       "          ...     \n",
       "530       0.036509\n",
       "531       0.019789\n",
       "532       0.036952\n",
       "533       0.053573\n",
       "534       0.008345\n",
       "Name: I, Length: 535, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb634ea9-479f-4483-8adf-8ef7f222e609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataframe.plot(\"q\", \"I\", yerr=\"uI\", loglog=True, ecolor=\"gray\", grid=True, label=fnData[0].name);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}