NetCDF Explorer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "import netCDF4\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "datafile = \"/Users/seth.urion/src/Skagit-IBM/reepicheep/test3/output_0.nc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = netCDF4.Dataset(datafile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sampleSiteID': <class 'netCDF4._netCDF4.Variable'>\n",
       " int32 sampleSiteID(sampleHistoryLength)\n",
       " unlimited dimensions: \n",
       " current shape = (408528,)\n",
       " filling on, default _FillValue of -2147483647 used,\n",
       " 'sampleTime': <class 'netCDF4._netCDF4.Variable'>\n",
       " int32 sampleTime(sampleHistoryLength)\n",
       " unlimited dimensions: \n",
       " current shape = (408528,)\n",
       " filling on, default _FillValue of -2147483647 used,\n",
       " 'samplePop': <class 'netCDF4._netCDF4.Variable'>\n",
       " int32 samplePop(sampleHistoryLength)\n",
       " unlimited dimensions: \n",
       " current shape = (408528,)\n",
       " filling on, default _FillValue of -2147483647 used,\n",
       " 'sampleMeanMass': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 sampleMeanMass(sampleHistoryLength)\n",
       " unlimited dimensions: \n",
       " current shape = (408528,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'sampleMeanLength': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 sampleMeanLength(sampleHistoryLength)\n",
       " unlimited dimensions: \n",
       " current shape = (408528,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'sampleMeanSpawnTime': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 sampleMeanSpawnTime(sampleHistoryLength)\n",
       " unlimited dimensions: \n",
       " current shape = (408528,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used}"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsx = xr.open_dataset(datafile)\n",
    "df = dsx.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./reepicheep/test3/output_0.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>sampleSiteID</th>\n",
       "      <th>sampleTime</th>\n",
       "      <th>samplePop</th>\n",
       "      <th>sampleMeanMass</th>\n",
       "      <th>sampleMeanLength</th>\n",
       "      <th>sampleMeanSpawnTime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sampleHistoryLength</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>1023</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.205475</td>\n",
       "      <td>48.640751</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>1025</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>1.047395</td>\n",
       "      <td>48.156700</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>1026</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>1.250425</td>\n",
       "      <td>51.620960</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>1027</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0.955323</td>\n",
       "      <td>48.291618</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>1029</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1.350991</td>\n",
       "      <td>52.030518</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375603</th>\n",
       "      <td>1119</td>\n",
       "      <td>3708</td>\n",
       "      <td>2</td>\n",
       "      <td>0.297112</td>\n",
       "      <td>30.533016</td>\n",
       "      <td>2217.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375640</th>\n",
       "      <td>1156</td>\n",
       "      <td>3708</td>\n",
       "      <td>1</td>\n",
       "      <td>0.456105</td>\n",
       "      <td>37.554329</td>\n",
       "      <td>2735.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376352</th>\n",
       "      <td>1868</td>\n",
       "      <td>3708</td>\n",
       "      <td>1</td>\n",
       "      <td>2.442499</td>\n",
       "      <td>60.101543</td>\n",
       "      <td>2182.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376396</th>\n",
       "      <td>1912</td>\n",
       "      <td>3708</td>\n",
       "      <td>1</td>\n",
       "      <td>0.535918</td>\n",
       "      <td>35.629768</td>\n",
       "      <td>2452.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376493</th>\n",
       "      <td>2009</td>\n",
       "      <td>3708</td>\n",
       "      <td>1</td>\n",
       "      <td>0.184126</td>\n",
       "      <td>29.252705</td>\n",
       "      <td>1415.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     sampleSiteID  sampleTime  samplePop  sampleMeanMass  \\\n",
       "sampleHistoryLength                                                        \n",
       "1023                         1023          12          1        1.205475   \n",
       "1025                         1025          12          7        1.047395   \n",
       "1026                         1026          12          4        1.250425   \n",
       "1027                         1027          12          2        0.955323   \n",
       "1029                         1029          12          2        1.350991   \n",
       "...                           ...         ...        ...             ...   \n",
       "375603                       1119        3708          2        0.297112   \n",
       "375640                       1156        3708          1        0.456105   \n",
       "376352                       1868        3708          1        2.442499   \n",
       "376396                       1912        3708          1        0.535918   \n",
       "376493                       2009        3708          1        0.184126   \n",
       "\n",
       "                     sampleMeanLength  sampleMeanSpawnTime  \n",
       "sampleHistoryLength                                         \n",
       "1023                        48.640751             3.000000  \n",
       "1025                        48.156700             0.857143  \n",
       "1026                        51.620960             1.000000  \n",
       "1027                        48.291618             1.500000  \n",
       "1029                        52.030518             1.500000  \n",
       "...                               ...                  ...  \n",
       "375603                      30.533016          2217.000000  \n",
       "375640                      37.554329          2735.000000  \n",
       "376352                      60.101543          2182.000000  \n",
       "376396                      35.629768          2452.000000  \n",
       "376493                      29.252705          1415.000000  \n",
       "\n",
       "[545 rows x 6 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.sampleMeanSpawnTime > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'recruitTime': <class 'netCDF4._netCDF4.Variable'>\n",
       " int32 recruitTime(n)\n",
       " unlimited dimensions: \n",
       " current shape = (6278381,)\n",
       " filling on, default _FillValue of -2147483647 used,\n",
       " 'exitTime': <class 'netCDF4._netCDF4.Variable'>\n",
       " int32 exitTime(n)\n",
       " unlimited dimensions: \n",
       " current shape = (6278381,)\n",
       " filling on, default _FillValue of -2147483647 used,\n",
       " 'entryForkLength': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 entryForkLength(n)\n",
       " unlimited dimensions: \n",
       " current shape = (6278381,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'entryMass': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 entryMass(n)\n",
       " unlimited dimensions: \n",
       " current shape = (6278381,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'finalForkLength': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 finalForkLength(n)\n",
       " unlimited dimensions: \n",
       " current shape = (6278381,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'finalMass': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 finalMass(n)\n",
       " unlimited dimensions: \n",
       " current shape = (6278381,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'finalStatus': <class 'netCDF4._netCDF4.Variable'>\n",
       " int32 finalStatus(n)\n",
       " unlimited dimensions: \n",
       " current shape = (6278381,)\n",
       " filling on, default _FillValue of -2147483647 used,\n",
       " 'monitoringPopulation': <class 'netCDF4._netCDF4.Variable'>\n",
       " int32 monitoringPopulation(monitoringPoints, historyLength)\n",
       " unlimited dimensions: \n",
       " current shape = (281, 3984)\n",
       " filling on, default _FillValue of -2147483647 used,\n",
       " 'monitoringDepth': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 monitoringDepth(monitoringPoints, historyLength)\n",
       " unlimited dimensions: \n",
       " current shape = (281, 3984)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'monitoringTemp': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 monitoringTemp(monitoringPoints, historyLength)\n",
       " unlimited dimensions: \n",
       " current shape = (281, 3984)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'monitoringPointIDs': <class 'netCDF4._netCDF4.Variable'>\n",
       " int32 monitoringPointIDs(monitoringPoints)\n",
       " unlimited dimensions: \n",
       " current shape = (281,)\n",
       " filling on, default _FillValue of -2147483647 used}"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_file = '/Users/seth.urion/src/Skagit-IBM/reepicheep/test3/summary_0.nc'\n",
    "# dsx2 = xr.open_dataset(big_file)\n",
    "# df2 = dsx2.to_dataframe()\n",
    "ds = netCDF4.Dataset(big_file)\n",
    "ds.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Variable'>\n",
       "int32 finalStatus(n)\n",
       "unlimited dimensions: \n",
       "current shape = (6278381,)\n",
       "filling on, default _FillValue of -2147483647 used"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['finalStatus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "status = ds.variables['finalStatus'][:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'MaskedArray'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[130], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m[\u001b[49m\u001b[43mstatus\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'MaskedArray'"
     ]
    }
   ],
   "source": [
    "ds.variables[status == 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "exited = ds.variables['finalStatus'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[3, 3, 3, ..., 0, 0, 0],\n",
       "             mask=False,\n",
       "       fill_value=999999,\n",
       "            dtype=int32)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exited"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = np.argwhere(ds.variables['finalStatus'][:] == 4)\n",
    "# print(xx)\n",
    "exited = []\n",
    "for x in xx:\n",
    "    # print(x)\n",
    "    exited.append(ds['finalForkLength'][x].data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = np.argwhere(ds.variables['finalStatus'][:] == 3)\n",
    "# print(xx)\n",
    "starved = []\n",
    "for x in xx:\n",
    "    # print(x)\n",
    "    starved.append(ds['finalForkLength'][x].data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = np.argwhere(ds.variables['finalStatus'][:] == 0)\n",
    "# print(xx)\n",
    "alive = []\n",
    "for x in xx:\n",
    "    # print(x)\n",
    "    alive.append(ds['finalForkLength'][x].data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = np.argwhere(ds.variables['finalStatus'][:] == 1)\n",
    "# print(xx)\n",
    "mort = []\n",
    "for x in xx:\n",
    "    # print(x)\n",
    "    mort.append(ds['finalForkLength'][x].data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exited:    516\n",
      "Starved:   4164167\n",
      "Alive:     2111937\n",
      "Mortality: 1761\n"
     ]
    }
   ],
   "source": [
    "print('Exited:    ' + str(len(exited)))\n",
    "print('Starved:   ' + str(len(starved)))\n",
    "print('Alive:     ' + str(len(alive)))\n",
    "print('Mortality: ' + str(len(mort)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(exited, [x for x in range(15, 100, 5)], color=\"orange\", edgecolor=\"black\")\n",
    "plt.yscale('log')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(starved, [x for x in range(15, 100, 5)], color=\"orange\", edgecolor=\"black\")\n",
    "plt.yscale('log')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(alive, [x for x in range(15, 100, 5)], color=\"orange\", edgecolor=\"black\")\n",
    "plt.yscale('log')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(mort, [x for x in range(15, 100, 5)], color=\"orange\", edgecolor=\"black\")\n",
    "plt.yscale('log')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'`status[n]`: ints, current/final status of each fish\\n        - 0: Alive\\n        - 1: Dead from mortality risk\\n        - 2: Dead from stranding\\n        - 3: Dead from starvation\\n        - 4: Exited'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"`status[n]`: ints, current/final status of each fish\n",
    "        - 0: Alive\n",
    "        - 1: Dead from mortality risk\n",
    "        - 2: Dead from stranding\n",
    "        - 3: Dead from starvation\n",
    "        - 4: Exited\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "type DataArray doesn't define __round__ method",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dsx2\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;28;43mround\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdsx2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfinalForkLength\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m)\u001b[38;5;241m.\u001b[39mcount()\n",
      "\u001b[0;31mTypeError\u001b[0m: type DataArray doesn't define __round__ method"
     ]
    }
   ],
   "source": [
    "dsx2.groupby(round(dsx2.finalForkLength, 0)).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36.83105 , 40.4159  , 32.07501 , ..., 53.74385 , 52.806934,\n",
       "       61.05795 ], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsx2.finalForkLength.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (finalForkLength_bins: 1, monitoringPoints: 281,\n",
       "                           historyLength: 3984)\n",
       "Coordinates:\n",
       "  * finalForkLength_bins  (finalForkLength_bins) object (75, 85]\n",
       "Dimensions without coordinates: monitoringPoints, historyLength\n",
       "Data variables:\n",
       "    recruitTime           (finalForkLength_bins) int64 2583\n",
       "    exitTime              (finalForkLength_bins) int64 2583\n",
       "    entryForkLength       (finalForkLength_bins) int64 2583\n",
       "    entryMass             (finalForkLength_bins) int64 2583\n",
       "    finalForkLength       (finalForkLength_bins) int64 2583\n",
       "    finalMass             (finalForkLength_bins) int64 2583\n",
       "    finalStatus           (finalForkLength_bins) int64 2583\n",
       "    monitoringPopulation  (finalForkLength_bins, monitoringPoints, historyLength) int64 ...\n",
       "    monitoringDepth       (finalForkLength_bins, monitoringPoints, historyLength) int64 ...\n",
       "    monitoringTemp        (finalForkLength_bins, monitoringPoints, historyLength) int64 ...\n",
       "    monitoringPointIDs    (finalForkLength_bins, monitoringPoints) int64 1 ... 1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-db3c5ca9-ef5a-47f7-a2fd-634ab36f8221' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-db3c5ca9-ef5a-47f7-a2fd-634ab36f8221' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>finalForkLength_bins</span>: 1</li><li><span>monitoringPoints</span>: 281</li><li><span>historyLength</span>: 3984</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-25410003-6991-4caf-90e3-f31941334f2a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25410003-6991-4caf-90e3-f31941334f2a' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>finalForkLength_bins</span></div><div class='xr-var-dims'>(finalForkLength_bins)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>(75, 85]</div><input id='attrs-0a08844e-09a8-477c-97bf-bc167bf35ccd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a08844e-09a8-477c-97bf-bc167bf35ccd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a046102-d1b0-4772-a921-cdf2d13fc0cd' class='xr-var-data-in' type='checkbox'><label for='data-0a046102-d1b0-4772-a921-cdf2d13fc0cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([Interval(75, 85, closed=&#x27;right&#x27;)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-542e456b-deed-421b-87fc-4e700bd87a2e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-542e456b-deed-421b-87fc-4e700bd87a2e' class='xr-section-summary' >Data variables: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>recruitTime</span></div><div class='xr-var-dims'>(finalForkLength_bins)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2583</div><input id='attrs-4cd8262e-6a0c-4eac-9e63-5c07af8906b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4cd8262e-6a0c-4eac-9e63-5c07af8906b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4756ffa8-0cff-4af3-b37e-6f68275c1947' class='xr-var-data-in' type='checkbox'><label for='data-4756ffa8-0cff-4af3-b37e-6f68275c1947' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2583])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>exitTime</span></div><div class='xr-var-dims'>(finalForkLength_bins)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2583</div><input id='attrs-a841db27-78ad-463b-90ad-cc7867e7d75a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a841db27-78ad-463b-90ad-cc7867e7d75a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a89c6298-6dba-4ee8-958f-019bea80eb42' class='xr-var-data-in' type='checkbox'><label for='data-a89c6298-6dba-4ee8-958f-019bea80eb42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2583])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>entryForkLength</span></div><div class='xr-var-dims'>(finalForkLength_bins)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2583</div><input id='attrs-8dcc9242-d088-446f-a4af-38edcce875a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8dcc9242-d088-446f-a4af-38edcce875a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4586a6e8-cb65-46d1-a0b0-850981358a37' class='xr-var-data-in' type='checkbox'><label for='data-4586a6e8-cb65-46d1-a0b0-850981358a37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2583])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>entryMass</span></div><div class='xr-var-dims'>(finalForkLength_bins)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2583</div><input id='attrs-07ebffb7-ed99-4bd1-b201-07b33fb1ee2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07ebffb7-ed99-4bd1-b201-07b33fb1ee2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cc9f2a4-e58d-4711-bfa4-cb606af05d22' class='xr-var-data-in' type='checkbox'><label for='data-2cc9f2a4-e58d-4711-bfa4-cb606af05d22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2583])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>finalForkLength</span></div><div class='xr-var-dims'>(finalForkLength_bins)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2583</div><input id='attrs-4b85367f-4982-4c10-b440-e731189952e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b85367f-4982-4c10-b440-e731189952e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc26f0d8-c63a-483b-b4f5-b09e311b190e' class='xr-var-data-in' type='checkbox'><label for='data-dc26f0d8-c63a-483b-b4f5-b09e311b190e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2583])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>finalMass</span></div><div class='xr-var-dims'>(finalForkLength_bins)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2583</div><input id='attrs-503c5b55-68e3-42be-8cea-e349ecf2ea9f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-503c5b55-68e3-42be-8cea-e349ecf2ea9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf539f58-7938-41f5-b61e-3db55d58fd1d' class='xr-var-data-in' type='checkbox'><label for='data-bf539f58-7938-41f5-b61e-3db55d58fd1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2583])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>finalStatus</span></div><div class='xr-var-dims'>(finalForkLength_bins)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2583</div><input id='attrs-c2bbda01-d929-4956-bded-f917b0f7a885' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2bbda01-d929-4956-bded-f917b0f7a885' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a30d0f2f-b221-4790-aec7-158801cbadad' class='xr-var-data-in' type='checkbox'><label for='data-a30d0f2f-b221-4790-aec7-158801cbadad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2583])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>monitoringPopulation</span></div><div class='xr-var-dims'>(finalForkLength_bins, monitoringPoints, historyLength)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-abb9d383-561a-4789-a43f-0a0f5b814c9d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abb9d383-561a-4789-a43f-0a0f5b814c9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43a7bfc9-9a03-41d7-9a85-05ee006e6848' class='xr-var-data-in' type='checkbox'><label for='data-43a7bfc9-9a03-41d7-9a85-05ee006e6848' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[1, 1, 1, ..., 1, 1, 1],\n",
       "        [1, 1, 1, ..., 1, 1, 1],\n",
       "        [1, 1, 1, ..., 1, 1, 1],\n",
       "        ...,\n",
       "        [1, 1, 1, ..., 1, 1, 1],\n",
       "        [1, 1, 1, ..., 1, 1, 1],\n",
       "        [1, 1, 1, ..., 1, 1, 1]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>monitoringDepth</span></div><div class='xr-var-dims'>(finalForkLength_bins, monitoringPoints, historyLength)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-aee68113-bc3d-4b7b-ba3c-6803d559d4c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aee68113-bc3d-4b7b-ba3c-6803d559d4c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfbbe585-9f0b-4c13-8553-fcaad31809e5' class='xr-var-data-in' type='checkbox'><label for='data-cfbbe585-9f0b-4c13-8553-fcaad31809e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[1, 1, 1, ..., 1, 1, 1],\n",
       "        [1, 1, 1, ..., 1, 1, 1],\n",
       "        [1, 1, 1, ..., 1, 1, 1],\n",
       "        ...,\n",
       "        [1, 1, 1, ..., 1, 1, 1],\n",
       "        [1, 1, 1, ..., 1, 1, 1],\n",
       "        [1, 1, 1, ..., 1, 1, 1]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>monitoringTemp</span></div><div class='xr-var-dims'>(finalForkLength_bins, monitoringPoints, historyLength)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-d5b5e8f7-61e6-40dd-8773-ab504b9f6917' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5b5e8f7-61e6-40dd-8773-ab504b9f6917' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd2c2b1c-4714-466e-be38-0f93b96ad5e2' class='xr-var-data-in' type='checkbox'><label for='data-dd2c2b1c-4714-466e-be38-0f93b96ad5e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[1, 1, 1, ..., 1, 1, 1],\n",
       "        [1, 1, 1, ..., 1, 1, 1],\n",
       "        [1, 1, 1, ..., 1, 1, 1],\n",
       "        ...,\n",
       "        [1, 1, 1, ..., 1, 1, 1],\n",
       "        [1, 1, 1, ..., 1, 1, 1],\n",
       "        [1, 1, 1, ..., 1, 1, 1]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>monitoringPointIDs</span></div><div class='xr-var-dims'>(finalForkLength_bins, monitoringPoints)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-1c1542e8-3b48-4910-9380-4c59b4ea307e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c1542e8-3b48-4910-9380-4c59b4ea307e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21b5c638-4220-4026-b131-27ee253f8fda' class='xr-var-data-in' type='checkbox'><label for='data-21b5c638-4220-4026-b131-27ee253f8fda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6cf5efd3-1108-4683-9ff0-98684b17b9e4' class='xr-section-summary-in' type='checkbox'  ><label for='section-6cf5efd3-1108-4683-9ff0-98684b17b9e4' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>finalForkLength_bins</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-89dd7bcd-d530-4d14-a83e-16775de0fae8' class='xr-index-data-in' type='checkbox'/><label for='index-89dd7bcd-d530-4d14-a83e-16775de0fae8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(IntervalIndex([(75, 85]], dtype=&#x27;interval[int64, right]&#x27;, name=&#x27;finalForkLength_bins&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3990a91f-a6ad-4550-870a-4a4ec58e2aea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3990a91f-a6ad-4550-870a-4a4ec58e2aea' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (finalForkLength_bins: 1, monitoringPoints: 281,\n",
       "                           historyLength: 3984)\n",
       "Coordinates:\n",
       "  * finalForkLength_bins  (finalForkLength_bins) object (75, 85]\n",
       "Dimensions without coordinates: monitoringPoints, historyLength\n",
       "Data variables:\n",
       "    recruitTime           (finalForkLength_bins) int64 2583\n",
       "    exitTime              (finalForkLength_bins) int64 2583\n",
       "    entryForkLength       (finalForkLength_bins) int64 2583\n",
       "    entryMass             (finalForkLength_bins) int64 2583\n",
       "    finalForkLength       (finalForkLength_bins) int64 2583\n",
       "    finalMass             (finalForkLength_bins) int64 2583\n",
       "    finalStatus           (finalForkLength_bins) int64 2583\n",
       "    monitoringPopulation  (finalForkLength_bins, monitoringPoints, historyLength) int64 ...\n",
       "    monitoringDepth       (finalForkLength_bins, monitoringPoints, historyLength) int64 ...\n",
       "    monitoringTemp        (finalForkLength_bins, monitoringPoints, historyLength) int64 ...\n",
       "    monitoringPointIDs    (finalForkLength_bins, monitoringPoints) int64 1 ... 1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fl_bins = [75, 85]\n",
    "dsx2.groupby_bins(dsx2.finalForkLength, fl_bins).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = np.histogram(np.array(dsx2.finalForkLength).astype(int), [15, 25, 35, 45, 55, 65, 75, 85, 95, 105, 115, 125])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.array(ds.variables['finalForkLength']).astype(int), [x for x in range(15, 100, 5)], color=\"salmon\", edgecolor=\"black\")\n",
    "plt.yscale('log')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(np.array(dsx2.finalForkLength).astype(int), [x for x in range(15, 100, 5)])\n",
    "plt.yscale('log')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>sampleSiteID</th>\n",
       "      <th>sampleTime</th>\n",
       "      <th>samplePop</th>\n",
       "      <th>sampleMeanMass</th>\n",
       "      <th>sampleMeanLength</th>\n",
       "      <th>sampleMeanSpawnTime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sampleHistoryLength</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408523</th>\n",
       "      <td>34039</td>\n",
       "      <td>3708</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408524</th>\n",
       "      <td>34040</td>\n",
       "      <td>3708</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408525</th>\n",
       "      <td>34041</td>\n",
       "      <td>3708</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408526</th>\n",
       "      <td>34042</td>\n",
       "      <td>3708</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408527</th>\n",
       "      <td>34043</td>\n",
       "      <td>3708</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408528 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     sampleSiteID  sampleTime  samplePop  sampleMeanMass  \\\n",
       "sampleHistoryLength                                                        \n",
       "0                               0          12          0             0.0   \n",
       "1                               1          12          0             0.0   \n",
       "2                               2          12          0             0.0   \n",
       "3                               3          12          0             0.0   \n",
       "4                               4          12          0             0.0   \n",
       "...                           ...         ...        ...             ...   \n",
       "408523                      34039        3708          0             0.0   \n",
       "408524                      34040        3708          0             0.0   \n",
       "408525                      34041        3708          0             0.0   \n",
       "408526                      34042        3708          0             0.0   \n",
       "408527                      34043        3708          0             0.0   \n",
       "\n",
       "                     sampleMeanLength  sampleMeanSpawnTime  \n",
       "sampleHistoryLength                                         \n",
       "0                                 0.0                  0.0  \n",
       "1                                 0.0                  0.0  \n",
       "2                                 0.0                  0.0  \n",
       "3                                 0.0                  0.0  \n",
       "4                                 0.0                  0.0  \n",
       "...                               ...                  ...  \n",
       "408523                            0.0                  0.0  \n",
       "408524                            0.0                  0.0  \n",
       "408525                            0.0                  0.0  \n",
       "408526                            0.0                  0.0  \n",
       "408527                            0.0                  0.0  \n",
       "\n",
       "[408528 rows x 6 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = pd.read_csv('data/sampling_nodes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Grain of Sand', 'FWP New Site', 'Browns Sl Barrow Ch',\n",
       "       'Browns Sl Diked Side', 'Ika Lower', 'Tom Moore', 'Ika Upper',\n",
       "       'Cattail Saltmarsh', 'DW Reference E Blind',\n",
       "       'Swin Ch Old Bridge Blind', 'Hall Sl Trib'], dtype=object)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes['Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', ' Node ID', ' Area (m^2)'], dtype='object')"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site Name                      m^2    nodes     ∑pop\n",
      "========================    ======   ======   ======\n",
      "Grain of Sand                 2381      132        0\n",
      "FWP New Site                   123       36        0\n",
      "Browns Sl Barrow Ch           1337      948        0\n",
      "Browns Sl Diked Side           444       12        0\n",
      "Ika Lower                      304      108        0\n",
      "Tom Moore                      514      468        0\n",
      "Ika Upper                    55744      936        0\n",
      "Cattail Saltmarsh             1378      180        0\n",
      "DW Reference E Blind          2045     1020        0\n",
      "Swin Ch Old Bridge Blind       503       96        0\n",
      "Hall Sl Trib                   201      204        0\n"
     ]
    }
   ],
   "source": [
    "print(f'{\"Site Name\" :25} {\"m^2\" :>8} {\"nodes\" :>8} {\"∑pop\" :>8}')\n",
    "print('========================    ======   ======   ======')\n",
    "for nodeName in nodes['Name'].unique():\n",
    "    nodeIDs = [x for x in nodes[nodes.Name == nodeName][' Node ID']]\n",
    "    print(f'{nodeName :25} {str(sum(nodes[nodes[' Node ID'].isin(nodeIDs)][' Area (m^2)'])) :>8} {str(len(df[df['sampleSiteID'].isin(nodeIDs)])) :>8} {str(sum(df[df['sampleSiteID'].isin(nodeIDs)]['samplePop'])) :>8}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sampleMeanLength</th>\n",
       "      <th>sampleMeanSpawnTime</th>\n",
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       "      <td>0.463795</td>\n",
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       "      <td>1053</td>\n",
       "      <td>3708</td>\n",
       "      <td>11</td>\n",
       "      <td>0.252241</td>\n",
       "      <td>31.180870</td>\n",
       "      <td>1785.363647</td>\n",
       "    </tr>\n",
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       "      <th>375539</th>\n",
       "      <td>1055</td>\n",
       "      <td>3708</td>\n",
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       "      <td>0.390933</td>\n",
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       "<p>116 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "                     sampleSiteID  sampleTime  samplePop  sampleMeanMass  \\\n",
       "sampleHistoryLength                                                        \n",
       "1025                         1025          12          7        1.047395   \n",
       "1026                         1026          12          4        1.250425   \n",
       "1031                         1031          12          8        1.079311   \n",
       "1032                         1032          12          4        1.189750   \n",
       "1034                         1034          12          4        1.120545   \n",
       "...                           ...         ...        ...             ...   \n",
       "375532                       1048        3708     664032        0.472228   \n",
       "375533                       1049        3708     718494        0.463795   \n",
       "375537                       1053        3708         11        0.252241   \n",
       "375539                       1055        3708       9306        0.390933   \n",
       "375548                       1064        3708          3        0.264856   \n",
       "\n",
       "                     sampleMeanLength  sampleMeanSpawnTime  \n",
       "sampleHistoryLength                                         \n",
       "1025                        48.156700             0.857143  \n",
       "1026                        51.620960             1.000000  \n",
       "1031                        47.987572             1.000000  \n",
       "1032                        48.450512             2.500000  \n",
       "1034                        48.452419             0.500000  \n",
       "...                               ...                  ...  \n",
       "375532                      36.552303          2311.418945  \n",
       "375533                      36.365662          2278.467773  \n",
       "375537                      31.180870          1785.363647  \n",
       "375539                      35.173481          2308.788086  \n",
       "375548                      32.960003          2291.333252  \n",
       "\n",
       "[116 rows x 6 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.samplePop > 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6024388"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df[df.sampleSiteID == 1049].samplePop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0            0\n",
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       "3            3\n",
       "4            4\n",
       "         ...  \n",
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       "34041    34041\n",
       "34042    34042\n",
       "34043    34043\n",
       "Name: sampleSiteID, Length: 34044, dtype: int32"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sampleSiteID.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                     sampleSiteID  sampleTime  samplePop  sampleMeanMass  \\\n",
       "sampleHistoryLength                                                        \n",
       "1013                         1013          12          0             0.0   \n",
       "4481                         4481          12          0             0.0   \n",
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       "...                           ...         ...        ...             ...   \n",
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       "400105                      25621        3708          0             0.0   \n",
       "400109                      25625        3708          0             0.0   \n",
       "\n",
       "                     sampleMeanLength  sampleMeanSpawnTime  \n",
       "sampleHistoryLength                                         \n",
       "1013                              0.0                  0.0  \n",
       "4481                              0.0                  0.0  \n",
       "4952                              0.0                  0.0  \n",
       "5385                              0.0                  0.0  \n",
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       "\n",
       "[936 rows x 6 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodeIDs = [x for x in nodes[nodes.Name == 'Ika Upper'][' Node ID']]\n",
    "df[(df.sampleSiteID.isin(nodeIDs))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
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       "      <th>103173</th>\n",
       "      <td>1041</td>\n",
       "      <td>1020</td>\n",
       "      <td>1209042</td>\n",
       "      <td>0.851617</td>\n",
       "      <td>44.979012</td>\n",
       "      <td>477.487579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103180</th>\n",
       "      <td>1048</td>\n",
       "      <td>1020</td>\n",
       "      <td>1207075</td>\n",
       "      <td>0.851547</td>\n",
       "      <td>44.979473</td>\n",
       "      <td>478.736969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103181</th>\n",
       "      <td>1049</td>\n",
       "      <td>1020</td>\n",
       "      <td>1232247</td>\n",
       "      <td>0.862417</td>\n",
       "      <td>45.163395</td>\n",
       "      <td>473.769287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307437</th>\n",
       "      <td>1041</td>\n",
       "      <td>3036</td>\n",
       "      <td>624727</td>\n",
       "      <td>0.681850</td>\n",
       "      <td>41.486202</td>\n",
       "      <td>2203.027588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307445</th>\n",
       "      <td>1049</td>\n",
       "      <td>3036</td>\n",
       "      <td>654694</td>\n",
       "      <td>0.677866</td>\n",
       "      <td>41.434814</td>\n",
       "      <td>2188.512451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341481</th>\n",
       "      <td>1041</td>\n",
       "      <td>3372</td>\n",
       "      <td>662227</td>\n",
       "      <td>0.563525</td>\n",
       "      <td>38.955555</td>\n",
       "      <td>2258.838379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341488</th>\n",
       "      <td>1048</td>\n",
       "      <td>3372</td>\n",
       "      <td>648497</td>\n",
       "      <td>0.568825</td>\n",
       "      <td>39.083633</td>\n",
       "      <td>2275.476807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341489</th>\n",
       "      <td>1049</td>\n",
       "      <td>3372</td>\n",
       "      <td>695363</td>\n",
       "      <td>0.559498</td>\n",
       "      <td>38.892853</td>\n",
       "      <td>2243.318359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375525</th>\n",
       "      <td>1041</td>\n",
       "      <td>3708</td>\n",
       "      <td>682417</td>\n",
       "      <td>0.466999</td>\n",
       "      <td>36.418621</td>\n",
       "      <td>2294.692139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375532</th>\n",
       "      <td>1048</td>\n",
       "      <td>3708</td>\n",
       "      <td>664032</td>\n",
       "      <td>0.472228</td>\n",
       "      <td>36.552303</td>\n",
       "      <td>2311.418945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375533</th>\n",
       "      <td>1049</td>\n",
       "      <td>3708</td>\n",
       "      <td>718494</td>\n",
       "      <td>0.463795</td>\n",
       "      <td>36.365662</td>\n",
       "      <td>2278.467773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     sampleSiteID  sampleTime  samplePop  sampleMeanMass  \\\n",
       "sampleHistoryLength                                                        \n",
       "69129                        1041         684     902194        0.925659   \n",
       "69136                        1048         684     902122        0.925092   \n",
       "69137                        1049         684     919900        0.933642   \n",
       "103173                       1041        1020    1209042        0.851617   \n",
       "103180                       1048        1020    1207075        0.851547   \n",
       "103181                       1049        1020    1232247        0.862417   \n",
       "307437                       1041        3036     624727        0.681850   \n",
       "307445                       1049        3036     654694        0.677866   \n",
       "341481                       1041        3372     662227        0.563525   \n",
       "341488                       1048        3372     648497        0.568825   \n",
       "341489                       1049        3372     695363        0.559498   \n",
       "375525                       1041        3708     682417        0.466999   \n",
       "375532                       1048        3708     664032        0.472228   \n",
       "375533                       1049        3708     718494        0.463795   \n",
       "\n",
       "                     sampleMeanLength  sampleMeanSpawnTime  \n",
       "sampleHistoryLength                                         \n",
       "69129                       46.157269           341.262299  \n",
       "69136                       46.151157           341.413300  \n",
       "69137                       46.285336           338.323700  \n",
       "103173                      44.979012           477.487579  \n",
       "103180                      44.979473           478.736969  \n",
       "103181                      45.163395           473.769287  \n",
       "307437                      41.486202          2203.027588  \n",
       "307445                      41.434814          2188.512451  \n",
       "341481                      38.955555          2258.838379  \n",
       "341488                      39.083633          2275.476807  \n",
       "341489                      38.892853          2243.318359  \n",
       "375525                      36.418621          2294.692139  \n",
       "375532                      36.552303          2311.418945  \n",
       "375533                      36.365662          2278.467773  "
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.samplePop > 0.5 * max(df.samplePop)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11723031"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df[df.samplePop > 0.5 * max(df.samplePop)]['samplePop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17639411"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df.samplePop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5916380"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df.samplePop) - sum(df[df.samplePop > 0.5 * max(df.samplePop)]['samplePop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  12,  348,  684, 1020, 1356, 1692, 2028, 2364, 2700, 3036, 3372,\n",
       "       3708], dtype=int32)"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sampleTime.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x114098950>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = []\n",
    "for t in df.sampleTime.unique():\n",
    "    p.append(sum(df[df.sampleTime == t]['samplePop']))\n",
    "\n",
    "plt.scatter(x=df.sampleTime.unique(), y=p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x174d69ca0>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q = []\n",
    "for t in df.sampleTime.unique():\n",
    "    q.append(np.mean(df[(df.sampleTime == t) & (df.sampleMeanLength != 0)]['sampleMeanLength']))\n",
    "plt.scatter(x = df.sampleTime.unique(), y = q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x174d4edb0>"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = []\n",
    "for t in df.sampleTime.unique():\n",
    "    r.append(len(df[(df.sampleTime == t) & (df.sampleMeanLength != 0)]['sampleMeanLength']))\n",
    "plt.scatter(x = df.sampleTime.unique(), y = r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x176a98e90>"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = []\n",
    "times = df.sampleTime.unique()\n",
    "for t in times:\n",
    "    s.append(len(df[(df.sampleTime == t)\n",
    "                         # & (df.sampleMeanLength != 0)\n",
    "                           & (df.sampleSiteID == 1306)\n",
    "                           ]['sampleMeanLength']))\n",
    "plt.scatter(x = times, y = s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.sqrt(df[df.samplePop > 0].samplePop), [x for x in range(1, 1000, 50)],  color=\"wheat\", edgecolor = \"black\")\n",
    "plt.yscale('log')\n",
    "# plt.xscale('log')\n",
    "plt.rcParams['figure.figsize'] = [12, 5]\n",
    "plt.grid(visible=True, axis='y', which='both')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.sqrt(df[df.samplePop >= -1].samplePop), [x for x in range(0, 10)],  color=\"wheat\", edgecolor = \"black\")\n",
    "plt.yscale('log')\n",
    "# plt.xscale('log')\n",
    "plt.rcParams['figure.figsize'] = [12, 5]\n",
    "plt.grid(visible=True, axis='y', which='major')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "recruits = pd.read_csv('data/skagit_migrants_2006.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day', 'month', 'year', 'abundance'], dtype='object')"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recruits.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6791239"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(recruits.abundance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17814c8f0>"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=recruits.day, y=recruits.abundance, color='black', marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "netcdf",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}