slope_estimation.ipynb

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       "<pre>✔️ 4.32 s (2025-01-01T00:11:15/2025-01-01T00:11:20)</pre>"
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   "source": [
    "%load_ext autotime\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "from glob import glob\n",
    "from shapely.geometry import LineString, Point\n",
    "import folium\n",
    "import matplotlib.pyplot as plt\n",
    "import pytz\n",
    "from datetime import datetime, timedelta\n",
    "pd.set_option('display.max_columns', None)\n",
    "import SDS_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "              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",
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       "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",
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       "aus0001-0004  19.528839   637.676213  25.252252  169.027861   \n",
       "...                 ...          ...        ...         ...   \n",
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       "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",
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       "...                                                         ...  \n",
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       "\n",
       "[127429 rows x 16 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transects = gpd.read_file(\"transects_extended.geojson\").set_index(\"id\")\n",
    "transects"
   ]
  },
  {
   "cell_type": "code",
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     "shell.execute_reply": "2024-12-31T11:11:23.960605Z"
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   "outputs": [
    {
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       "<pre>✔️ 50.8 ms (2025-01-01T00:11:23/2025-01-01T00:11:23)</pre>"
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       "Empty GeoDataFrame\n",
       "Columns: [site_id, orientation, along_dist, along_dist_norm, beach_slope, cil, ciu, trend, n_points, n_points_nonan, r2_score, mae, mse, rmse, intercept, geometry]\n",
       "Index: []"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_transects = transects[transects.index.str.startswith(\"nzd\") & (transects.index > \"nzd0562\") & transects.beach_slope.isna()]\n",
    "new_transects"
   ]
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     "data": {
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       "<pre>✔️ 2.32 ms (2025-01-01T00:11:23/2025-01-01T00:11:23)</pre>"
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   "source": [
    "if len(new_transects):\n",
    "  for site_id in tqdm(new_transects.site_id.unique()):\n",
    "    df = pd.read_csv(f\"data/{site_id}/transect_time_series.csv\")\n",
    "    df.index = pd.to_datetime(df.dates)\n",
    "    df.drop(columns=[\"dates\", \"satname\"], inplace=True)\n",
    "    tides = pd.read_csv(\"data/{site_id}/tides.csv\")\n",
    "    tides.dates = pd.to_datetime(tides.dates)\n",
    "    tides.set_index(\"dates\", inplace=True)\n",
    "    assert all(pd.to_datetime(df.index).round(\"10min\") == tides.index)\n",
    "    # slope estimation settings\n",
    "    days_in_year = 365.2425\n",
    "    seconds_in_day = 24*3600\n",
    "    settings_slope = {'slope_min':        0.01,                  # minimum slope to trial\n",
    "                      'slope_max':        0.2,                    # maximum slope to trial\n",
    "                      'delta_slope':      0.005,                  # slope increment\n",
    "                      'date_range':       [1999,2020],            # range of dates over which to perform the analysis\n",
    "                      'n_days':           8,                      # sampling period [days]\n",
    "                      'n0':               50,                     # parameter for Nyquist criterium in Lomb-Scargle transforms\n",
    "                      'freqs_cutoff':     1./(seconds_in_day*30), # 1 month frequency\n",
    "                      'delta_f':          100*1e-10,              # deltaf for identifying peak tidal frequency band\n",
    "                      'prc_conf':         0.05,                   # percentage above minimum to define confidence bands in energy curve\n",
    "                      }\n",
    "    settings_slope['date_range'] = [pytz.utc.localize(datetime(settings_slope['date_range'][0],5,1)),\n",
    "                                    pytz.utc.localize(datetime(settings_slope['date_range'][1],1,1))]\n",
    "    beach_slopes = SDS_slope.range_slopes(settings_slope['slope_min'], settings_slope['slope_max'], settings_slope['delta_slope'])\n",
    "\n",
    "    t = np.array([_.timestamp() for _ in df.index]).astype('float64')\n",
    "    delta_t = np.diff(t)\n",
    "    fig, ax = plt.subplots(1,1,figsize=(12,3), tight_layout=True)\n",
    "    ax.grid(which='major', linestyle=':', color='0.5')\n",
    "    bins = np.arange(np.min(delta_t)/seconds_in_day, np.max(delta_t)/seconds_in_day+1,1)-0.5\n",
    "    ax.hist(delta_t/seconds_in_day, bins=bins, ec='k', width=1);\n",
    "    ax.set(xlabel='timestep [days]', ylabel='counts',\n",
    "          xticks=7*np.arange(0,20),\n",
    "          xlim=[0,50], title='Timestep distribution');\n",
    "\n",
    "    # find tidal peak frequency (can choose 7 or 8 in this case)\n",
    "    settings_slope['n_days'] = 7\n",
    "    settings_slope['freqs_max'] = SDS_slope.find_tide_peak(df.index,tides.tide,settings_slope)\n",
    "    # estimate beach-face slopes along the transects\n",
    "    slope_est, cis = dict([]), dict([])\n",
    "    for key in df.keys():\n",
    "        # remove NaNs\n",
    "        idx_nan = np.isnan(df[key])\n",
    "        dates = [df.index[_] for _ in np.where(~idx_nan)[0]]\n",
    "        tide = tides.tide.to_numpy()[~idx_nan]\n",
    "        composite = df[key][~idx_nan]\n",
    "        # apply tidal correction\n",
    "        tsall = SDS_slope.tide_correct(composite,tide,beach_slopes)\n",
    "        title = 'Transect %s'%key\n",
    "        SDS_slope.plot_spectrum_all(dates,composite,tsall,settings_slope, title)\n",
    "        slope_est[key],cis[key] = SDS_slope.integrate_power_spectrum(dates,tsall,settings_slope)\n",
    "        print('Beach slope at transect %s: %.3f'%(key, slope_est[key]))\n",
    "    transects.beach_slope.update(slope_est)\n",
    "    transects.cil.update({k: v[0] for k,v in cis.items()})\n",
    "    transects.ciu.update({k: v[1] for k,v in cis.items()})\n",
    "    transects[transects.index.isin(slope_est.keys())]\n",
    "  transects.to_file(\"transects_extended.geojson\")"
   ]
  }
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