read_mie_data_10m.ipynb

{
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     "text": [
      "time: 138 µs (started: 2023-07-24 10:02:47 +02:00)\n"
     ]
    }
   ],
   "source": [
    "%load_ext autotime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "907bfd07-21a4-4b73-b913-ba8387dec580",
   "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "time: 2.34 s (started: 2023-07-24 10:02:47 +02:00)\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import dask.dataframe as dd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from joblib import Parallel, delayed\n",
    "import multiprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32fe3909-0562-4140-a542-88effc44bb92",
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    {
     "data": {
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       "256"
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     "execution_count": 3,
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "time: 2.93 ms (started: 2023-07-24 10:02:49 +02:00)\n"
     ]
    }
   ],
   "source": [
    "num_cores = multiprocessing.cpu_count()\n",
    "num_cores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "10050883-c35f-4cd5-9085-4ed0ce5db173",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.41 ms (started: 2023-07-24 10:02:49 +02:00)\n"
     ]
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   ],
   "source": [
    "def read_data(filenames):\n",
    "    df = pd.DataFrame([], dtype=\"float\")\n",
    "    columns = []\n",
    "    for i, filename in enumerate(filenames):\n",
    "        columns.append(int(filenames[i].split('_')[-1].split('.')[0]))\n",
    "        df1 = pd.read_csv(filenames[i], index_col=0)\n",
    "        df = pd.concat([df, df1], axis=1)\n",
    "    df.columns = columns\n",
    "    df = df.T        \n",
    "    return df\n",
    "\n",
    "def read_data1(lst, outpath):\n",
    "    df = read_data(lst)\n",
    "    df.to_csv(outpath, mode='a', index=True, float_format='%.5f')\n",
    "\n",
    "def get_parallel_list(rand_idx, num_cores=1000):\n",
    "    num = len(np.array_split(rand_idx, num_cores-5, axis=0)[0])\n",
    "    new_list = np.array_split(rand_idx, num, axis=0)\n",
    "    return new_list "
   ]
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   "cell_type": "code",
   "execution_count": 5,
   "id": "52609189-0bc8-4a7a-905f-ca6b8189a581",
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "time: 19.1 s (started: 2023-07-24 10:02:49 +02:00)\n"
     ]
    }
   ],
   "source": [
    "filenames1 = sorted(glob.glob('/work/bb1070/b382177/mie/icon/18-07-2023/x1/mie_*.csv'))\n",
    "filenames2 = sorted(glob.glob('/work/bb1070/b382177/mie/icon/18-07-2023/x2/mie_*.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e2c5644-b63e-47d1-ac1d-acda3146b816",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 7.02 s (started: 2023-07-24 10:03:08 +02:00)\n"
     ]
    }
   ],
   "source": [
    "new_list1 = get_parallel_list(filenames1, num_cores=250)\n",
    "new_list2 = get_parallel_list(filenames2, num_cores=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8be20f15-0932-4027-96b4-2ca75d2d54e5",
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       " ...]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5min 33s (started: 2023-07-24 10:03:16 +02:00)\n"
     ]
    }
   ],
   "source": [
    "Parallel(n_jobs=num_cores)(delayed(read_data1)(lst, outpath='/work/bb1070/b382177/mie/icon/24-07-2023/mie_x1.csv') for lst in new_list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "36c95793-c2b2-40db-998d-47cdb755069f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
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       " ...]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3min 53s (started: 2023-07-24 10:08:49 +02:00)\n"
     ]
    }
   ],
   "source": [
    "Parallel(n_jobs=num_cores)(delayed(read_data1)(lst, outpath='/work/bb1070/b382177/mie/icon/24-07-2023/mie_x2.csv') for lst in new_list2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ec6aca7d-3571-4ebc-a97f-bc540ca8e90c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1240571/270521990.py:1: DtypeWarning: Columns (1,2,3,4,5,6,7,8,9,10,11,12,13,14) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df1 = pd.read_csv('/work/bb1070/b382177/mie/icon/24-07-2023/mie_x1.csv', index_col=0).sort_index().iloc[:-23339, :]\n"
     ]
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       "      <th>Extinction</th>\n",
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       "                  Extinction           Scattering          Absorption  \\\n",
       "0                   0.072795             0.002253            0.070542   \n",
       "2                   0.034139             0.000141            0.033998   \n",
       "8                   0.007768             0.000024            0.007744   \n",
       "11                  0.003332             0.000005            0.003327   \n",
       "18                  0.001833                  0.0            0.001832   \n",
       "...                      ...                  ...                 ...   \n",
       "44527776  0.8441136588707878  0.10897719156677485  0.7351364673040129   \n",
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       "44527903  0.6712109240212665   0.0687112889884546  0.6024996350328119   \n",
       "\n",
       "                         Asym                  SSA coating core_dia    lambda  \\\n",
       "0                    0.019297             0.030951     0.0     10.0     100.0   \n",
       "2                    0.004843             0.004118     0.0     10.0     200.0   \n",
       "8                    0.002135             0.003152     0.0     10.0     300.0   \n",
       "11                   0.000946             0.001404     0.0     10.0     450.0   \n",
       "18                   0.000299             0.000253     0.0     10.0     800.0   \n",
       "...                       ...                  ...     ...      ...       ...   \n",
       "44527776  0.08338609171483982  0.12910250938547657     0.4  10000.0   75000.0   \n",
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       "44527839   0.0890801355049577    0.138397569912788     0.4  11000.0   80000.0   \n",
       "44527902  0.08398412766421275  0.12977268317397833     0.4  12000.0   90000.0   \n",
       "44527903  0.06785948546632262  0.10236914586669864     0.4  12000.0  100000.0   \n",
       "\n",
       "           n_core      k_core             n_shell             k_shell density  \\\n",
       "0           1.486      0.1061               1.464                 0.0     2.6   \n",
       "2           1.486      0.1061               1.396                 0.0     2.6   \n",
       "8           1.463      0.0359               1.349                 0.0     2.6   \n",
       "11        1.45504    0.023075            1.336727                 0.0     2.6   \n",
       "18        1.45299    0.022598            1.329723                 0.0     2.6   \n",
       "...           ...         ...                 ...                 ...     ...   \n",
       "44527776   1.6135  0.64809787              2.0175  0.7224999999999999     1.7   \n",
       "44527837   1.6135  0.64809787  2.0149999999999997               0.725     1.7   \n",
       "44527839   1.6135  0.64809787                2.02                0.72     1.7   \n",
       "44527902   1.6135  0.64809787                2.02               0.685     1.7   \n",
       "44527903   1.6135  0.64809787                2.02                0.65     1.7   \n",
       "\n",
       "         nobackscat         x  \n",
       "0              True  0.314159  \n",
       "2              True  0.157080  \n",
       "8              True  0.104720  \n",
       "11             True  0.069813  \n",
       "18             True  0.039270  \n",
       "...             ...       ...  \n",
       "44527776       True       NaN  \n",
       "44527837       True       NaN  \n",
       "44527839       True       NaN  \n",
       "44527902       True       NaN  \n",
       "44527903       True       NaN  \n",
       "\n",
       "[14667786 rows x 15 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2min 10s (started: 2023-07-24 10:32:06 +02:00)\n"
     ]
    }
   ],
   "source": [
    "df1 = pd.read_csv('/work/bb1070/b382177/mie/icon/24-07-2023/mie_x1.csv', index_col=0).sort_index().iloc[:-23339, :]\n",
    "df1.index = df1.index.astype('int')\n",
    "\n",
    "df2 = pd.read_csv('/work/bb1070/b382177/mie/icon/24-07-2023/mie_x2.csv', index_col=0).sort_index().iloc[:8300000, :]\n",
    "df2.index = df2.index.astype('int')\n",
    "\n",
    "df = pd.concat([df1, df2], axis=0).drop_duplicates()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9f95c592-3f53-4a8d-9c1d-5aceded22499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.76 s (started: 2023-07-24 10:34:38 +02:00)\n"
     ]
    }
   ],
   "source": [
    "df['x'] = np.pi*df['core_dia'].astype('float') / df['lambda'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c594ed25-4a50-4428-9f72-8cb6c41f5568",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1min 42s (started: 2023-07-24 10:34:43 +02:00)\n"
     ]
    }
   ],
   "source": [
    "df.sort_index().to_csv('/work/bb1070/b382177/mie/icon/24-07-2023/mie_all.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "44b55ceb-a90d-4e0d-b290-e84731572852",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x2400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.26 s (started: 2023-07-24 10:36:25 +02:00)\n"
     ]
    }
   ],
   "source": [
    "col = 'x'\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(24, 24))\n",
    "df[col].astype('float').hist(grid=False, bins=np.arange(0, 0.1, 0.001), ax=ax1)\n",
    "df[col].astype('float').hist(grid=False, bins=np.arange(0.1, 1, 0.01), ax=ax2)\n",
    "df[col].astype('float').hist(grid=False, bins=np.arange(1, 101, 1), ax=ax3)\n",
    "df[col].astype('float').hist(grid=False, bins=np.arange(100, 601, 5), ax=ax4)\n",
    "plt.savefig('/work/bb1070/b382177/mie/icon/24-07-2023/figs/distribution_size_param.png', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1efb979a-9ae6-4b35-b673-0567fab420b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 4.89 s (started: 2023-07-24 10:36:30 +02:00)\n"
     ]
    }
   ],
   "source": [
    "col = 'core_dia'\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(20, 18))\n",
    "df[col].astype('float').hist(grid=False, bins=np.arange(8, 101, 2), ax=ax1)\n",
    "df[col].astype('float').hist(grid=False, bins=np.arange(100, 910, 10), ax=ax2)\n",
    "df[col].astype('float').hist(grid=False, bins=np.arange(1000, 20100, 200), ax=ax3)\n",
    "plt.savefig('/work/bb1070/b382177/mie/icon/24-07-2023/figs/distribution_core_dia.png', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "99b428e3-f32b-44fa-86e8-dfe7ee2d9c07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qVbfnOzs7s5tuuimrqKjISktLs09+8pPZ888/3xenwmEkn+t29+7d2bnnnpt98IMfzI444ojsxBNPzGbNmuUvksm7XNbtxo0bs4jY63HWWWcd8DEhH/K9bqdPn54NHz48KykpyUaOHJlNnz49e+mll/rwjDgc5LJuTzzxxB7XbWNjY9cYn2/pC/lctz7f0ldyWbc33HBDdsopp2RlZWXZcccdl9XU1GQrVqzodjzXWwotn2vWtZa+kuvfgf2pniKIa23fKMqyLOvb754AAAAAAAAUnnuCAAAAAAAASRJBAAAAAACAJIkgAAAAAABAkkQQAAAAAAAgSSIIAAAAAACQJBEEAAAAAABIkggCAAAAAAAkSQQBAAAAAACSJIIAAAAAAABJEkEAAAAAAIAkiSAAAAAAAECS/h89gfsr93BwiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.53 s (started: 2023-07-24 10:36:35 +02:00)\n"
     ]
    }
   ],
   "source": [
    "col = 'coating'\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(20, 6))\n",
    "df[col].astype('float').hist(grid=False, bins=np.arange(0, 0.41, 0.005), ax=ax1)\n",
    "plt.savefig('/work/bb1070/b382177/mie/icon/24-07-2023/figs/distribution_coating.png', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Mie",
   "language": "python",
   "name": "mie"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}