linear_models.ipynb

{
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       "<pre>✔️ 874 ms (2024-12-03T11:39:05/2024-12-03T11:39:05)</pre>"
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   "source": [
    "%reload_ext autotime\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from tqdm.auto import tqdm\n",
    "from tqdm.contrib.concurrent import process_map\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error, root_mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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     "shell.execute_reply": "2024-12-02T05:39:55.433706Z"
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       "<pre>✔️ 3.61 s (2024-12-03T11:39:06/2024-12-03T11:39:09)</pre>"
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       "      <th></th>\n",
       "      <th>site_id</th>\n",
       "      <th>orientation</th>\n",
       "      <th>along_dist</th>\n",
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       "      <th>beach_slope</th>\n",
       "      <th>cil</th>\n",
       "      <th>ciu</th>\n",
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       "      <th>mse</th>\n",
       "      <th>rmse</th>\n",
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       "      <td>654.0</td>\n",
       "      <td>491.0</td>\n",
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       "      <td>22.896464</td>\n",
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       "      <th>aus0001-0003</th>\n",
       "      <td>aus0001</td>\n",
       "      <td>81.192757</td>\n",
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       "      <th>nzd0563-0002</th>\n",
       "      <td>nzd0563</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0624</td>\n",
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      ],
      "text/plain": [
       "              site_id  orientation  along_dist  along_dist_norm  beach_slope  \\\n",
       "id                                                                             \n",
       "aus0001-0000  aus0001   104.347648    0.000000         0.000000        0.085   \n",
       "aus0001-0001  aus0001    93.495734   98.408334         0.002935        0.050   \n",
       "aus0001-0002  aus0001    82.069341  198.408334         0.005918        0.050   \n",
       "aus0001-0003  aus0001    81.192757  298.402523         0.008900        0.055   \n",
       "aus0001-0004  aus0001    81.065473  398.402523         0.011882        0.075   \n",
       "...               ...          ...         ...              ...          ...   \n",
       "nzd0562-0017  nzd0562          NaN         NaN              NaN        0.055   \n",
       "nzd0562-0018  nzd0562          NaN         NaN              NaN        0.045   \n",
       "nzd0563-0000  nzd0563          NaN         NaN              NaN        0.200   \n",
       "nzd0563-0001  nzd0563          NaN         NaN              NaN        0.050   \n",
       "nzd0563-0002  nzd0563          NaN         NaN              NaN        0.070   \n",
       "\n",
       "                 cil     ciu     trend  n_points  n_points_nonan  r2_score  \\\n",
       "id                                                                           \n",
       "aus0001-0000  0.0545  0.2000 -1.456305     654.0           342.0  0.131951   \n",
       "aus0001-0001  0.0387  0.0640 -0.833581     654.0           473.0  0.052783   \n",
       "aus0001-0002  0.0428  0.0647 -0.282209     654.0           491.0  0.008220   \n",
       "aus0001-0003  0.0480  0.0659  0.089340     654.0           502.0  0.000987   \n",
       "aus0001-0004  0.0614  0.0922  0.361437     654.0           508.0  0.017461   \n",
       "...              ...     ...       ...       ...             ...       ...   \n",
       "nzd0562-0017  0.0541  0.0608  0.006797     582.0           407.0  0.000019   \n",
       "nzd0562-0018  0.0434  0.0461  0.284894     582.0           372.0  0.022977   \n",
       "nzd0563-0000  0.1756  0.2000 -0.286535     770.0           684.0  0.020982   \n",
       "nzd0563-0001  0.0323  0.1200 -0.072549     770.0           614.0  0.003174   \n",
       "nzd0563-0002  0.0624  0.0742 -0.260641     770.0           632.0  0.026072   \n",
       "\n",
       "                    mae          mse       rmse   intercept  \\\n",
       "id                                                            \n",
       "aus0001-0000  28.721214  1321.059410  36.346381  179.407277   \n",
       "aus0001-0001  26.029482  1092.336862  33.050520  210.629160   \n",
       "aus0001-0002  22.896464   838.997233  28.965449  200.166307   \n",
       "aus0001-0003  20.770475   691.179232  26.290288  185.295473   \n",
       "aus0001-0004  19.528839   637.676213  25.252252  169.027861   \n",
       "...                 ...          ...        ...         ...   \n",
       "nzd0562-0017   8.181382   127.722282  11.301428  212.322965   \n",
       "nzd0562-0018   9.773159   187.352460  13.687675  216.532589   \n",
       "nzd0563-0000  12.984757   214.309394  14.639310  182.918496   \n",
       "nzd0563-0001   8.484539    95.454639   9.770089  437.079989   \n",
       "nzd0563-0002   9.013683   143.900611  11.995858  173.017496   \n",
       "\n",
       "                                                       geometry  \n",
       "id                                                               \n",
       "aus0001-0000  LINESTRING (153.26555 -24.7007, 153.26938 -24....  \n",
       "aus0001-0001  LINESTRING (153.26525 -24.7019, 153.2692 -24.7...  \n",
       "aus0001-0002  LINESTRING (153.26539 -24.70316, 153.26931 -24...  \n",
       "aus0001-0003  LINESTRING (153.26555 -24.70408, 153.26945 -24...  \n",
       "aus0001-0004  LINESTRING (153.2657 -24.70497, 153.26961 -24....  \n",
       "...                                                         ...  \n",
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       "nzd0562-0018  LINESTRING (174.90891 -36.86889, 174.91293 -36...  \n",
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       "nzd0563-0001  LINESTRING (176.93648 -39.33459, 176.93708 -39...  \n",
       "nzd0563-0002  LINESTRING (176.93431 -39.33569, 176.93792 -39...  \n",
       "\n",
       "[127429 rows x 16 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transects, origin is landward. Has beach_slope\n",
    "transects = gpd.read_file(\"transects_extended.geojson\")\n",
    "transects.set_index(\"id\", inplace=True)\n",
    "transects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "execution": {
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     "shell.execute_reply": "2024-12-02T05:39:55.588040Z"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>✔️ 33.5 ms (2024-12-03T11:39:09/2024-12-03T11:39:09)</pre>"
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    {
     "data": {
      "text/plain": [
       "0       shoreline_data_run6/aus0001/time_series_tidall...\n",
       "1       shoreline_data_run6/aus0002/time_series_tidall...\n",
       "2       shoreline_data_run6/aus0003/time_series_tidall...\n",
       "3       shoreline_data_run6/aus0004/time_series_tidall...\n",
       "4       shoreline_data_run6/aus0005/time_series_tidall...\n",
       "                              ...                        \n",
       "3064    shoreline_data_run6/usa_HI_0051/time_series_ti...\n",
       "3065    shoreline_data_run6/usa_HI_0052/time_series_ti...\n",
       "3066    shoreline_data_run6/usa_HI_0053/time_series_ti...\n",
       "3067    shoreline_data_run6/usa_HI_0054/time_series_ti...\n",
       "3068    shoreline_data_run6/usa_HI_0057/time_series_ti...\n",
       "Length: 2509, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vos_files = pd.Series(sorted(glob(\"shoreline_data_run6/*/time_series_tidally_corrected.csv\")))\n",
    "vos_files = vos_files[~vos_files.str.contains(\"nzd\")]\n",
    "vos_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "shell.execute_reply": "2024-12-02T05:39:55.609722Z"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>✔️ 10.6 ms (2024-12-03T11:39:09/2024-12-03T11:39:09)</pre>"
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      "text/plain": [
       "0      data/nzd0001/transect_time_series_tidally_corr...\n",
       "1      data/nzd0002/transect_time_series_tidally_corr...\n",
       "2      data/nzd0003/transect_time_series_tidally_corr...\n",
       "3      data/nzd0004/transect_time_series_tidally_corr...\n",
       "4      data/nzd0005/transect_time_series_tidally_corr...\n",
       "                             ...                        \n",
       "557    data/nzd0559/transect_time_series_tidally_corr...\n",
       "558    data/nzd0560/transect_time_series_tidally_corr...\n",
       "559    data/nzd0561/transect_time_series_tidally_corr...\n",
       "560    data/nzd0562/transect_time_series_tidally_corr...\n",
       "561    data/nzd0563/transect_time_series_tidally_corr...\n",
       "Length: 562, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_nz_files = pd.Series(sorted(glob(\"data/*/transect_time_series_tidally_corrected.csv\")))\n",
    "my_nz_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-02T05:39:55.612942Z",
     "iopub.status.busy": "2024-12-02T05:39:55.612352Z",
     "iopub.status.idle": "2024-12-02T05:39:55.617946Z",
     "shell.execute_reply": "2024-12-02T05:39:55.617247Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>✔️ 1.88 s (2024-12-03T11:39:10/2024-12-03T11:39:11)</pre>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0      shoreline_data_run6/aus0001/time_series_tidall...\n",
       "1      shoreline_data_run6/aus0002/time_series_tidall...\n",
       "2      shoreline_data_run6/aus0003/time_series_tidall...\n",
       "3      shoreline_data_run6/aus0004/time_series_tidall...\n",
       "4      shoreline_data_run6/aus0005/time_series_tidall...\n",
       "                             ...                        \n",
       "557    data/nzd0559/transect_time_series_tidally_corr...\n",
       "558    data/nzd0560/transect_time_series_tidally_corr...\n",
       "559    data/nzd0561/transect_time_series_tidally_corr...\n",
       "560    data/nzd0562/transect_time_series_tidally_corr...\n",
       "561    data/nzd0563/transect_time_series_tidally_corr...\n",
       "Length: 3071, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = pd.concat([vos_files, my_nz_files])\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-02T05:39:55.620540Z",
     "iopub.status.busy": "2024-12-02T05:39:55.619935Z",
     "iopub.status.idle": "2024-12-02T05:40:10.157123Z",
     "shell.execute_reply": "2024-12-02T05:40:10.156062Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>✔️ 10.6 s (2024-12-03T11:39:12/2024-12-03T11:39:22)</pre>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1106770/2761047807.py:30: TqdmWarning: Iterable length 3071 > 1000 but `chunksize` is not set. This may seriously degrade multiprocess performance. Set `chunksize=1` or more.\n",
      "  trends = pd.concat(process_map(get_trends, files)).set_index(\"transect_id\")\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "48c89d2f230140b3b82aa228d5b1ea73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3071 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "121933"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_trends(f):\n",
    "  df = pd.read_csv(f)\n",
    "  try:\n",
    "    df.dates = pd.to_datetime(df.dates)\n",
    "  except:\n",
    "    print(f)\n",
    "  df.index = (df.dates - df.dates.min()).dt.days / 365.25\n",
    "  df.drop(columns=[\"dates\", \"satname\", \"Unnamed: 0\"], inplace=True, errors='ignore')\n",
    "  trends = []\n",
    "  for transect_id in df.columns:\n",
    "    sub_df = df[transect_id].dropna()\n",
    "    if not len(sub_df):\n",
    "      continue\n",
    "    x = sub_df.index.to_numpy().reshape(-1, 1)\n",
    "    y = sub_df\n",
    "    linear_model = LinearRegression().fit(x, y)\n",
    "    pred = linear_model.predict(x)\n",
    "    trends.append({\n",
    "      \"transect_id\": transect_id,\n",
    "      \"trend\": linear_model.coef_[0],\n",
    "      \"intercept\": linear_model.intercept_,\n",
    "      \"n_points\": len(df[transect_id]),\n",
    "      \"n_points_nonan\": len(sub_df),\n",
    "      \"r2_score\": r2_score(y, pred),\n",
    "      \"mae\": mean_absolute_error(y, pred),\n",
    "      \"mse\": mean_squared_error(y, pred),\n",
    "      \"rmse\": root_mean_squared_error(y, pred),\n",
    "    })\n",
    "  return pd.DataFrame(trends)\n",
    "trends = pd.concat(process_map(get_trends, files)).set_index(\"transect_id\")\n",
    "len(trends)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-02T05:40:10.162738Z",
     "iopub.status.busy": "2024-12-02T05:40:10.161536Z",
     "iopub.status.idle": "2024-12-02T05:40:10.213776Z",
     "shell.execute_reply": "2024-12-02T05:40:10.212845Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>✔️ 70.3 ms (2024-12-03T11:39:22/2024-12-03T11:39:22)</pre>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>trend</th>\n",
       "      <th>intercept</th>\n",
       "      <th>n_points</th>\n",
       "      <th>n_points_nonan</th>\n",
       "      <th>r2_score</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>rmse</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transect_id</th>\n",
       "      <th></th>\n",
       "      <th></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>nzd0380-0001</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>396.986919</td>\n",
       "      <td>526</td>\n",
       "      <td>434</td>\n",
       "      <td>3.487322e-12</td>\n",
       "      <td>10.354421</td>\n",
       "      <td>180.112991</td>\n",
       "      <td>13.420618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aus0213-0000</th>\n",
       "      <td>-0.000003</td>\n",
       "      <td>211.475107</td>\n",
       "      <td>543</td>\n",
       "      <td>487</td>\n",
       "      <td>8.622880e-12</td>\n",
       "      <td>7.797467</td>\n",
       "      <td>94.136738</td>\n",
       "      <td>9.702409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chi0406-0028</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>183.818769</td>\n",
       "      <td>456</td>\n",
       "      <td>396</td>\n",
       "      <td>1.916567e-11</td>\n",
       "      <td>6.755653</td>\n",
       "      <td>85.309469</td>\n",
       "      <td>9.236312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mex0157-0034</th>\n",
       "      <td>-0.000008</td>\n",
       "      <td>225.225194</td>\n",
       "      <td>496</td>\n",
       "      <td>413</td>\n",
       "      <td>3.072342e-11</td>\n",
       "      <td>10.834152</td>\n",
       "      <td>183.654195</td>\n",
       "      <td>13.551907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usa_CA_0052-0040</th>\n",
       "      <td>-0.000010</td>\n",
       "      <td>226.224974</td>\n",
       "      <td>1340</td>\n",
       "      <td>971</td>\n",
       "      <td>5.089262e-11</td>\n",
       "      <td>10.782984</td>\n",
       "      <td>177.958484</td>\n",
       "      <td>13.340108</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mex0004-0007</th>\n",
       "      <td>-7.151559</td>\n",
       "      <td>422.208551</td>\n",
       "      <td>453</td>\n",
       "      <td>432</td>\n",
       "      <td>9.769006e-01</td>\n",
       "      <td>8.743639</td>\n",
       "      <td>126.509611</td>\n",
       "      <td>11.247649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mex0004-0005</th>\n",
       "      <td>-7.365062</td>\n",
       "      <td>424.584864</td>\n",
       "      <td>453</td>\n",
       "      <td>411</td>\n",
       "      <td>9.769376e-01</td>\n",
       "      <td>9.308165</td>\n",
       "      <td>136.176728</td>\n",
       "      <td>11.669479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mex0004-0006</th>\n",
       "      <td>-7.311809</td>\n",
       "      <td>424.851589</td>\n",
       "      <td>453</td>\n",
       "      <td>424</td>\n",
       "      <td>9.771574e-01</td>\n",
       "      <td>9.168770</td>\n",
       "      <td>132.126977</td>\n",
       "      <td>11.494650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mex0367-0006</th>\n",
       "      <td>-17.743690</td>\n",
       "      <td>766.555581</td>\n",
       "      <td>1049</td>\n",
       "      <td>459</td>\n",
       "      <td>9.779147e-01</td>\n",
       "      <td>8.905623</td>\n",
       "      <td>142.902009</td>\n",
       "      <td>11.954163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mex0004-0010</th>\n",
       "      <td>-6.855499</td>\n",
       "      <td>411.578585</td>\n",
       "      <td>453</td>\n",
       "      <td>409</td>\n",
       "      <td>9.788066e-01</td>\n",
       "      <td>8.176001</td>\n",
       "      <td>109.845603</td>\n",
       "      <td>10.480725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121932 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      trend   intercept  n_points  n_points_nonan  \\\n",
       "transect_id                                                         \n",
       "nzd0380-0001       0.000003  396.986919       526             434   \n",
       "aus0213-0000      -0.000003  211.475107       543             487   \n",
       "chi0406-0028       0.000005  183.818769       456             396   \n",
       "mex0157-0034      -0.000008  225.225194       496             413   \n",
       "usa_CA_0052-0040  -0.000010  226.224974      1340             971   \n",
       "...                     ...         ...       ...             ...   \n",
       "mex0004-0007      -7.151559  422.208551       453             432   \n",
       "mex0004-0005      -7.365062  424.584864       453             411   \n",
       "mex0004-0006      -7.311809  424.851589       453             424   \n",
       "mex0367-0006     -17.743690  766.555581      1049             459   \n",
       "mex0004-0010      -6.855499  411.578585       453             409   \n",
       "\n",
       "                      r2_score        mae         mse       rmse  \n",
       "transect_id                                                       \n",
       "nzd0380-0001      3.487322e-12  10.354421  180.112991  13.420618  \n",
       "aus0213-0000      8.622880e-12   7.797467   94.136738   9.702409  \n",
       "chi0406-0028      1.916567e-11   6.755653   85.309469   9.236312  \n",
       "mex0157-0034      3.072342e-11  10.834152  183.654195  13.551907  \n",
       "usa_CA_0052-0040  5.089262e-11  10.782984  177.958484  13.340108  \n",
       "...                        ...        ...         ...        ...  \n",
       "mex0004-0007      9.769006e-01   8.743639  126.509611  11.247649  \n",
       "mex0004-0005      9.769376e-01   9.308165  136.176728  11.669479  \n",
       "mex0004-0006      9.771574e-01   9.168770  132.126977  11.494650  \n",
       "mex0367-0006      9.779147e-01   8.905623  142.902009  11.954163  \n",
       "mex0004-0010      9.788066e-01   8.176001  109.845603  10.480725  \n",
       "\n",
       "[121932 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trends[trends.n_points_nonan > 10].sort_values(\"r2_score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-02T05:40:10.217865Z",
     "iopub.status.busy": "2024-12-02T05:40:10.217557Z",
     "iopub.status.idle": "2024-12-02T05:40:10.269628Z",
     "shell.execute_reply": "2024-12-02T05:40:10.268762Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>✔️ 60 ms (2024-12-03T11:39:22/2024-12-03T11:39:22)</pre>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>trend</th>\n",
       "      <th>intercept</th>\n",
       "      <th>n_points</th>\n",
       "      <th>n_points_nonan</th>\n",
       "      <th>r2_score</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>121933.000000</td>\n",
       "      <td>121933.000000</td>\n",
       "      <td>121933.000000</td>\n",
       "      <td>121933.000000</td>\n",
       "      <td>1.219330e+05</td>\n",
       "      <td>121933.000000</td>\n",
       "      <td>121933.000000</td>\n",
       "      <td>121933.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.234233</td>\n",
       "      <td>238.692519</td>\n",
       "      <td>557.253631</td>\n",
       "      <td>436.155840</td>\n",
       "      <td>1.706041e-01</td>\n",
       "      <td>12.952105</td>\n",
       "      <td>355.154927</td>\n",
       "      <td>16.376453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.027113</td>\n",
       "      <td>97.395974</td>\n",
       "      <td>256.045250</td>\n",
       "      <td>209.935107</td>\n",
       "      <td>2.190084e-01</td>\n",
       "      <td>7.520278</td>\n",
       "      <td>673.613495</td>\n",
       "      <td>9.325633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-61.721935</td>\n",
       "      <td>-2765.025713</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.487322e-12</td>\n",
       "      <td>3.181092</td>\n",
       "      <td>16.314089</td>\n",
       "      <td>4.039070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.279281</td>\n",
       "      <td>185.689478</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>1.501543e-02</td>\n",
       "      <td>8.693111</td>\n",
       "      <td>122.057271</td>\n",
       "      <td>11.047953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.128640</td>\n",
       "      <td>217.607824</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>7.247818e-02</td>\n",
       "      <td>11.126523</td>\n",
       "      <td>197.550134</td>\n",
       "      <td>14.055253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.614909</td>\n",
       "      <td>314.021427</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>536.000000</td>\n",
       "      <td>2.420070e-01</td>\n",
       "      <td>14.775802</td>\n",
       "      <td>347.321528</td>\n",
       "      <td>18.636564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>89.734302</td>\n",
       "      <td>2329.832271</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1334.000000</td>\n",
       "      <td>9.788066e-01</td>\n",
       "      <td>172.354249</td>\n",
       "      <td>57339.880098</td>\n",
       "      <td>239.457470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               trend      intercept       n_points  n_points_nonan  \\\n",
       "count  121933.000000  121933.000000  121933.000000   121933.000000   \n",
       "mean        0.234233     238.692519     557.253631      436.155840   \n",
       "std         2.027113      97.395974     256.045250      209.935107   \n",
       "min       -61.721935   -2765.025713      56.000000       10.000000   \n",
       "25%        -0.279281     185.689478     371.000000      282.000000   \n",
       "50%         0.128640     217.607824     511.000000      414.000000   \n",
       "75%         0.614909     314.021427     672.000000      536.000000   \n",
       "max        89.734302    2329.832271    1567.000000     1334.000000   \n",
       "\n",
       "           r2_score            mae            mse           rmse  \n",
       "count  1.219330e+05  121933.000000  121933.000000  121933.000000  \n",
       "mean   1.706041e-01      12.952105     355.154927      16.376453  \n",
       "std    2.190084e-01       7.520278     673.613495       9.325633  \n",
       "min    3.487322e-12       3.181092      16.314089       4.039070  \n",
       "25%    1.501543e-02       8.693111     122.057271      11.047953  \n",
       "50%    7.247818e-02      11.126523     197.550134      14.055253  \n",
       "75%    2.420070e-01      14.775802     347.321528      18.636564  \n",
       "max    9.788066e-01     172.354249   57339.880098     239.457470  "
      ]
     },
     "execution_count": 8,
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     "output_type": "execute_result"
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   "source": [
    "trends.describe()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
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    "(transects.trend - trends.trend).describe()"
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  {
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      "text/html": [
       "<pre>✔️ 99.7 ms (2024-12-03T11:39:23/2024-12-03T11:39:23)</pre>"
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  {
   "cell_type": "code",
   "execution_count": 11,
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>site_id</th>\n",
       "      <th>orientation</th>\n",
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       "      <td>NaN</td>\n",
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       "<p>127429 rows × 16 columns</p>\n",
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      ],
      "text/plain": [
       "              site_id  orientation  along_dist  along_dist_norm  beach_slope  \\\n",
       "id                                                                             \n",
       "aus0001-0000  aus0001   104.347648    0.000000         0.000000        0.085   \n",
       "aus0001-0001  aus0001    93.495734   98.408334         0.002935        0.050   \n",
       "aus0001-0002  aus0001    82.069341  198.408334         0.005918        0.050   \n",
       "aus0001-0003  aus0001    81.192757  298.402523         0.008900        0.055   \n",
       "aus0001-0004  aus0001    81.065473  398.402523         0.011882        0.075   \n",
       "...               ...          ...         ...              ...          ...   \n",
       "nzd0562-0017  nzd0562          NaN         NaN              NaN        0.055   \n",
       "nzd0562-0018  nzd0562          NaN         NaN              NaN        0.045   \n",
       "nzd0563-0000  nzd0563          NaN         NaN              NaN        0.200   \n",
       "nzd0563-0001  nzd0563          NaN         NaN              NaN        0.050   \n",
       "nzd0563-0002  nzd0563          NaN         NaN              NaN        0.070   \n",
       "\n",
       "                 cil     ciu     trend  n_points  n_points_nonan  r2_score  \\\n",
       "id                                                                           \n",
       "aus0001-0000  0.0545  0.2000 -1.456305     654.0           342.0  0.131951   \n",
       "aus0001-0001  0.0387  0.0640 -0.833581     654.0           473.0  0.052783   \n",
       "aus0001-0002  0.0428  0.0647 -0.282209     654.0           491.0  0.008220   \n",
       "aus0001-0003  0.0480  0.0659  0.089340     654.0           502.0  0.000987   \n",
       "aus0001-0004  0.0614  0.0922  0.361437     654.0           508.0  0.017461   \n",
       "...              ...     ...       ...       ...             ...       ...   \n",
       "nzd0562-0017  0.0541  0.0608  0.006797     582.0           407.0  0.000019   \n",
       "nzd0562-0018  0.0434  0.0461  0.284894     582.0           372.0  0.022977   \n",
       "nzd0563-0000  0.1756  0.2000 -0.286535     770.0           684.0  0.020982   \n",
       "nzd0563-0001  0.0323  0.1200 -0.072549     770.0           614.0  0.003174   \n",
       "nzd0563-0002  0.0624  0.0742 -0.260641     770.0           632.0  0.026072   \n",
       "\n",
       "                    mae          mse       rmse   intercept  \\\n",
       "id                                                            \n",
       "aus0001-0000  28.721214  1321.059410  36.346381  179.407277   \n",
       "aus0001-0001  26.029482  1092.336862  33.050520  210.629160   \n",
       "aus0001-0002  22.896464   838.997233  28.965449  200.166307   \n",
       "aus0001-0003  20.770475   691.179232  26.290288  185.295473   \n",
       "aus0001-0004  19.528839   637.676213  25.252252  169.027861   \n",
       "...                 ...          ...        ...         ...   \n",
       "nzd0562-0017   8.181382   127.722282  11.301428  212.322965   \n",
       "nzd0562-0018   9.773159   187.352460  13.687675  216.532589   \n",
       "nzd0563-0000  12.984757   214.309394  14.639310  182.918496   \n",
       "nzd0563-0001   8.484539    95.454639   9.770089  437.079989   \n",
       "nzd0563-0002   9.013683   143.900611  11.995858  173.017496   \n",
       "\n",
       "                                                       geometry  \n",
       "id                                                               \n",
       "aus0001-0000  LINESTRING (153.26555 -24.7007, 153.26938 -24....  \n",
       "aus0001-0001  LINESTRING (153.26525 -24.7019, 153.2692 -24.7...  \n",
       "aus0001-0002  LINESTRING (153.26539 -24.70316, 153.26931 -24...  \n",
       "aus0001-0003  LINESTRING (153.26555 -24.70408, 153.26945 -24...  \n",
       "aus0001-0004  LINESTRING (153.2657 -24.70497, 153.26961 -24....  \n",
       "...                                                         ...  \n",
       "nzd0562-0017  LINESTRING (174.9093 -36.86951, 174.91331 -36....  \n",
       "nzd0562-0018  LINESTRING (174.90891 -36.86889, 174.91293 -36...  \n",
       "nzd0563-0000  LINESTRING (176.9374 -39.3345, 176.938 -39.33807)  \n",
       "nzd0563-0001  LINESTRING (176.93648 -39.33459, 176.93708 -39...  \n",
       "nzd0563-0002  LINESTRING (176.93431 -39.33569, 176.93792 -39...  \n",
       "\n",
       "[127429 rows x 16 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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       "(Index(['trend', 'intercept', 'n_points', 'n_points_nonan', 'r2_score', 'mae',\n",
       "        'mse', 'rmse'],\n",
       "       dtype='object'),\n",
       " Index(['site_id', 'orientation', 'along_dist', 'along_dist_norm',\n",
       "        'beach_slope', 'cil', 'ciu', 'trend', 'n_points', 'n_points_nonan',\n",
       "        'r2_score', 'mae', 'mse', 'rmse', 'intercept', 'geometry'],\n",
       "       dtype='object'),\n",
       " array([ True,  True,  True,  True,  True,  True,  True,  True]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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    }
   ],
   "source": [
    "trends.columns, transects.columns, trends.columns.isin(transects.columns)"
   ]
  },
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       "<pre>⌛ 3.56 µs (2024-12-03T11:39:23)</pre>"
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       "      <th></th>\n",
       "      <th>site_id</th>\n",
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       "      <th>beach_slope</th>\n",
       "      <th>cil</th>\n",
       "      <th>ciu</th>\n",
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      ],
      "text/plain": [
       "              site_id  orientation  along_dist  along_dist_norm  beach_slope  \\\n",
       "id                                                                             \n",
       "aus0001-0000  aus0001   104.347648    0.000000         0.000000        0.085   \n",
       "aus0001-0001  aus0001    93.495734   98.408334         0.002935        0.050   \n",
       "aus0001-0002  aus0001    82.069341  198.408334         0.005918        0.050   \n",
       "aus0001-0003  aus0001    81.192757  298.402523         0.008900        0.055   \n",
       "aus0001-0004  aus0001    81.065473  398.402523         0.011882        0.075   \n",
       "...               ...          ...         ...              ...          ...   \n",
       "nzd0562-0017  nzd0562          NaN         NaN              NaN        0.055   \n",
       "nzd0562-0018  nzd0562          NaN         NaN              NaN        0.045   \n",
       "nzd0563-0000  nzd0563          NaN         NaN              NaN        0.200   \n",
       "nzd0563-0001  nzd0563          NaN         NaN              NaN        0.050   \n",
       "nzd0563-0002  nzd0563          NaN         NaN              NaN        0.070   \n",
       "\n",
       "                 cil     ciu     trend  n_points  n_points_nonan  r2_score  \\\n",
       "id                                                                           \n",
       "aus0001-0000  0.0545  0.2000 -1.456305     654.0           342.0  0.131951   \n",
       "aus0001-0001  0.0387  0.0640 -0.833581     654.0           473.0  0.052783   \n",
       "aus0001-0002  0.0428  0.0647 -0.282209     654.0           491.0  0.008220   \n",
       "aus0001-0003  0.0480  0.0659  0.089340     654.0           502.0  0.000987   \n",
       "aus0001-0004  0.0614  0.0922  0.361437     654.0           508.0  0.017461   \n",
       "...              ...     ...       ...       ...             ...       ...   \n",
       "nzd0562-0017  0.0541  0.0608  0.006797     582.0           407.0  0.000019   \n",
       "nzd0562-0018  0.0434  0.0461  0.284894     582.0           372.0  0.022977   \n",
       "nzd0563-0000  0.1756  0.2000 -0.286535     770.0           684.0  0.020982   \n",
       "nzd0563-0001  0.0323  0.1200 -0.072549     770.0           614.0  0.003174   \n",
       "nzd0563-0002  0.0624  0.0742 -0.260641     770.0           632.0  0.026072   \n",
       "\n",
       "                    mae          mse       rmse   intercept  \\\n",
       "id                                                            \n",
       "aus0001-0000  28.721214  1321.059410  36.346381  179.407277   \n",
       "aus0001-0001  26.029482  1092.336862  33.050520  210.629160   \n",
       "aus0001-0002  22.896464   838.997233  28.965449  200.166307   \n",
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       "aus0001-0004  19.528839   637.676213  25.252252  169.027861   \n",
       "...                 ...          ...        ...         ...   \n",
       "nzd0562-0017   8.181382   127.722282  11.301428  212.322965   \n",
       "nzd0562-0018   9.773159   187.352460  13.687675  216.532589   \n",
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       "nzd0563-0001   8.484539    95.454639   9.770089  437.079989   \n",
       "nzd0563-0002   9.013683   143.900611  11.995858  173.017496   \n",
       "\n",
       "                                                       geometry  \n",
       "id                                                               \n",
       "aus0001-0000  LINESTRING (153.26555 -24.7007, 153.26938 -24....  \n",
       "aus0001-0001  LINESTRING (153.26525 -24.7019, 153.2692 -24.7...  \n",
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       "...                                                         ...  \n",
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     "execution_count": 13,
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   "source": [
    "transects = transects.join(trends.loc[:,~trends.columns.isin(transects.columns)])\n",
    "transects"
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