river_domain.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# River as a shapefile (raw format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>rid</th>\n",
       "      <th>netID</th>\n",
       "      <th>upDist</th>\n",
       "      <th>shreve</th>\n",
       "      <th>Length</th>\n",
       "      <th>addfunccol</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>LINESTRING (0.00000 1.00000, 0.00000 0.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.960106</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.960106</td>\n",
       "      <td>0.25</td>\n",
       "      <td>LINESTRING (0.42592 1.86046, 0.00000 1.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.980141</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.980141</td>\n",
       "      <td>0.75</td>\n",
       "      <td>LINESTRING (0.16583 1.96601, 0.00000 1.00000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3.032648</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.052508</td>\n",
       "      <td>0.25</td>\n",
       "      <td>LINESTRING (0.05008 3.01213, 0.16583 1.96601)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3.072281</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.092141</td>\n",
       "      <td>0.50</td>\n",
       "      <td>LINESTRING (0.75061 2.88840, 0.16583 1.96601)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rid netID    upDist  shreve    Length  addfunccol  \\\n",
       "0    1     1  1.000000     4.0  1.000000        1.00   \n",
       "1    2     1  1.960106     1.0  0.960106        0.25   \n",
       "2    3     1  1.980141     3.0  0.980141        0.75   \n",
       "3    4     1  3.032648     1.0  1.052508        0.25   \n",
       "4    5     1  3.072281     2.0  1.092141        0.50   \n",
       "\n",
       "                                        geometry  \n",
       "0  LINESTRING (0.00000 1.00000, 0.00000 0.00000)  \n",
       "1  LINESTRING (0.42592 1.86046, 0.00000 1.00000)  \n",
       "2  LINESTRING (0.16583 1.96601, 0.00000 1.00000)  \n",
       "3  LINESTRING (0.05008 3.01213, 0.16583 1.96601)  \n",
       "4  LINESTRING (0.75061 2.88840, 0.16583 1.96601)  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "\n",
    "shapefile = gpd.read_file(\"edges.shp\")\n",
    "shapefile[\"rid\"] = shapefile[\"rid\"].astype(int)\n",
    "shapefile.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rid - unique identifier for river segment\\\n",
    "netID - river network ID, when dealing with multiple river outlets\\\n",
    "upDist - distance upstream from outlet (from the most upstream point on line)\\\n",
    "shreve - segment weighting based on Shreve's order\\\n",
    "addfunccol - additive function column, normalised shreve's order (used for weighting segments, a proxy for water volume)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# edges (rid's)\n",
    "ax = shapefile.plot()\n",
    "shapefile.apply(\n",
    "    lambda x: ax.annotate(text=x[\"rid\"], xy=x.geometry.centroid.coords[0], ha=\"center\"),\n",
    "    axis=1,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract point information from shapely object: type(shapefile.geometry[0])\n",
    "shapefile[\"points\"] = shapefile.apply(lambda l: list(l[\"geometry\"].coords), axis=1)\n",
    "shapefile[[\"points_from\", \"points_to\"]] = shapefile[\"points\"].to_list()\n",
    "\n",
    "# create a unique identifier for the start/end points of an edge\n",
    "unique_locations = sorted(\n",
    "    list(set([item for sublist in shapefile.points.to_list() for item in sublist]))\n",
    ")\n",
    "PID_GPT = {i: unique_locations[i] for i in range(len(unique_locations))}\n",
    "GPT_PID = {v: k for k, v in PID_GPT.items()}\n",
    "\n",
    "# map from geographic point, GPT, to unique node identifier, PID\n",
    "shapefile[\"pid_from\"] = shapefile[\"points_from\"].apply(lambda x: GPT_PID[x])\n",
    "shapefile[\"pid_to\"] = shapefile[\"points_to\"].apply(lambda x: GPT_PID[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# node labels (pid's)\n",
    "ax = shapefile.plot()\n",
    "shapefile.apply(\n",
    "    lambda x: ax.annotate(text=x[\"pid_from\"], xy=x.points_from, ha=\"center\"), axis=1\n",
    ")\n",
    "shapefile.apply(\n",
    "    lambda x: ax.annotate(text=x[\"pid_to\"], xy=x.points_to, ha=\"center\"), axis=1\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# neither of these functions are necessary, now I realise\n",
    "def get_upstream_rids(row, df):\n",
    "    upstream_rids = []\n",
    "    up = df[df[\"pid_to\"] == row[\"pid_from\"]]\n",
    "\n",
    "    while len(up) > 0:\n",
    "        new_pids = list(up[\"pid_from\"])\n",
    "        upstream_rids.extend(up[\"rid\"])\n",
    "        up = df[df[\"pid_to\"].isin(new_pids)]\n",
    "\n",
    "    return upstream_rids\n",
    "\n",
    "\n",
    "def calculate_on_left(df):\n",
    "    grouped = df.groupby(\"pid_to\")\n",
    "    min_pid_from = grouped[\"pid_from\"].transform(\"min\")\n",
    "    df[\"on_left\"] = min_pid_from == df[\"pid_from\"]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = shapefile[\n",
    "    [\"rid\", \"upDist\", \"shreve\", \"Length\", \"addfunccol\", \"points\", \"pid_from\", \"pid_to\"]\n",
    "].copy()\n",
    "\n",
    "df[\"upstream_rids\"] = df.apply(lambda x: get_upstream_rids(x, df), axis=1)\n",
    "df = calculate_on_left(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## River domain representation (continuous)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mapping from segment to space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pid = 1  # the outlet node\n",
    "df.loc[df[\"pid_to\"] == pid, [\"parent_x_offset\"]] = 0\n",
    "df.loc[df[\"pid_to\"] == pid, [\"parent_width\"]] = 1\n",
    "\n",
    "from_pids_line_up = []\n",
    "finished = False\n",
    "\n",
    "while not finished:\n",
    "    # of length either 1 or 2\n",
    "    if len(df[df[\"pid_from\"] == pid]) > 0:\n",
    "        df.loc[df[\"pid_to\"] == pid, \"parent_x_offset\"] = float(\n",
    "            df[df[\"pid_from\"] == pid][\"x_offset\"].iloc[0]\n",
    "        )\n",
    "        df.loc[df[\"pid_to\"] == pid, \"parent_width\"] = float(\n",
    "            df[df[\"pid_from\"] == pid][\"width\"].iloc[0]\n",
    "        )\n",
    "\n",
    "    layer = df[df[\"pid_to\"] == pid].copy()\n",
    "    layer[\"pct\"] = layer[\"addfunccol\"] / layer[\"addfunccol\"].sum()\n",
    "    from_pids = layer[\"pid_from\"]\n",
    "\n",
    "    if len(from_pids) == 0:\n",
    "        finished = len(from_pids_line_up) == 0\n",
    "        if not finished:\n",
    "            pid = from_pids_line_up.pop(0)\n",
    "        continue\n",
    "\n",
    "    # add percentage share of addfunccol at each branching confluence\n",
    "    df.loc[df[\"pid_from\"].isin(from_pids), [\"width\"]] = (\n",
    "        layer[\"parent_width\"] * layer[\"pct\"]\n",
    "    )\n",
    "\n",
    "    # for the branch on left, then right\n",
    "    df.loc[df[\"pid_from\"] == min(from_pids), [\"x_offset\"]] = layer[\"parent_x_offset\"]\n",
    "\n",
    "    if len(from_pids) == 2:\n",
    "        df.loc[df[\"pid_from\"] == max(from_pids), [\"x_offset\"]] = float(\n",
    "            layer[\"parent_x_offset\"].iloc[0]\n",
    "        ) + float(df.loc[df[\"pid_from\"] == min(from_pids), [\"width\"]].squeeze())\n",
    "\n",
    "    # get next pid's\n",
    "    from_pids_line_up.extend(list(from_pids))\n",
    "    finished = len(from_pids_line_up) == 0\n",
    "    pid = from_pids_line_up.pop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bar\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for i, row in df.iterrows():\n",
    "    ax.bar(\n",
    "        row[\"x_offset\"],\n",
    "        height=row[\"Length\"],\n",
    "        bottom=row[\"upDist\"] - row[\"Length\"],\n",
    "        width=row[\"addfunccol\"] - 0.02,  # epsilon\n",
    "        align=\"edge\",\n",
    "        color=\"skyblue\",\n",
    "        alpha=0.5,\n",
    "        edgecolor=\"black\",\n",
    "    )\n",
    "    ax.annotate(\n",
    "        row[\"rid\"],\n",
    "        xy=(\n",
    "            row[\"x_offset\"] + 0.5 * row[\"addfunccol\"],\n",
    "            row[\"upDist\"] - 0.5 * row[\"Length\"],\n",
    "        ),\n",
    "    )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### River domain representation (as a shapely.Polygon object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "epsilon = 0.02\n",
    "df[\"box_coord\"] = [list() for i in range(len(df))]\n",
    "\n",
    "for i, row in df.iterrows():\n",
    "    x_left = row[\"x_offset\"]\n",
    "    x_right = row[\"x_offset\"] + row[\"addfunccol\"] - epsilon\n",
    "    y_top = row[\"upDist\"]\n",
    "    y_bottom = row[\"upDist\"] - row[\"Length\"]\n",
    "\n",
    "    df.loc[i, \"box_coord\"].extend(\n",
    "        [\n",
    "            (x_left, y_bottom),\n",
    "            (x_left, y_top),\n",
    "            (x_right, y_top),\n",
    "            (x_right, y_bottom),\n",
    "        ]\n",
    "    )  # bottom-left, top-left, top-right, bottom-right (clockwise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import groupby\n",
    "\n",
    "\n",
    "def group_by_x(tuples):\n",
    "    sorted_tuples = sorted(tuples, key=lambda point: point[0])\n",
    "    groups = []\n",
    "\n",
    "    for _, group in groupby(sorted_tuples, key=lambda point: point[0]):\n",
    "        groups.append(list(group))\n",
    "\n",
    "    return groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_points = sorted([item for sublist in df[\"box_coord\"].values for item in sublist])\n",
    "grouped_x = group_by_x(all_points)\n",
    "\n",
    "new_list = []\n",
    "for i, l in enumerate(grouped_x):\n",
    "    if i % 2 == 0:\n",
    "        new_list.append(l[0])\n",
    "        new_list.append(l[-1])\n",
    "    else:\n",
    "        new_list.append(l[-1])\n",
    "        new_list.append(l[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create high-coverage continous domain representation\n",
    "from shapely.geometry import Polygon\n",
    "from shapely.plotting import plot_polygon, plot_points\n",
    "\n",
    "fig = plt.figure(1, dpi=90)\n",
    "ax = fig.add_subplot()\n",
    "int = new_list + [new_list[0]]\n",
    "polygon = Polygon(int)\n",
    "\n",
    "plot_polygon(polygon, ax=ax, add_points=False, color=\"b\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0, 0.98, 4.044050746411085)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# any shapely helper functions??? inside shape? distance?\n",
    "polygon.bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# implement in pyro\n",
    "# alpha and sigma_0, sigma_1 are constant (remove Xbeta, temporal) -> only v random (learn the function)\n",
    "\n",
    "\n",
    "# 1. first get design (in network domain)\n",
    "# 2. compute h_ij matrix given design\n",
    "# 3. calculate covariance matrix for v, deterministic on h_ij\n",
    "# 4. sample v, using same pyro syntax as latent variable\n",
    "# 5. sample y given v with independent random noise (small, fixed) [mu=v, cov=Isigma]\n",
    "\n",
    "# strip out all location finding latent variables, put in stream distance\n",
    "\n",
    "# learning the design\n",
    "# gradient (nudge back within polygon), baseline: batch bald - discretization of design space add points one at a time, approximation to optimal design (read paper)\n",
    "\n",
    "# diagnostics, convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 18:41:39 From  Desi R. Ivanova  to  Everyone:\n",
    "# \tBatchBALD\n",
    "# 18:41:50 From  Adam  to  Everyone:\n",
    "# \thttps://arxiv.org/abs/1906.08158\n",
    "# 18:48:50 From  Adam  to  Everyone:\n",
    "# \thttps://mlflow.org/docs/latest/index.html"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear regression models that describe spatio-temporal dependence are generally formulated as:\n",
    "\n",
    "$\\bf y = \\bf X \\boldsymbol \\beta + \\bf v + \\boldsymbol \\epsilon$\n",
    "\n",
    "where $\\bf y$ is a stacked response vector of size $n \\times 1$, with $n = S \\times T$ (for $S$ spatial locations and $T$ timepoints), $\\bf X$ is a $n \\times p$ design matrix of $p$ covariates, $\\boldsymbol \\beta$ is a $p \\times 1$ vector or regression coefficients, and $\\bf v$ is a vector of spatio-temporal autocorrelated random effects.\n",
    "\n",
    "\n",
    "\\\n",
    "Two main approaches are generally found to model spatio-temporal autocorrelation: \n",
    "\n",
    "(1) full covariance or descriptive models in which the space-time covariance function is constructed (i.e. Separable space-time VAR(1) model), and \\\n",
    "(2) dynamical model that involves the evolution of a spatial process (i.e. Vector autoregression spatial model)\n",
    "\n",
    "In the second approach we just need to construct the spatial covariance matrix. Temporal dependence is incorporated in the error terms using a vector autoregression VAR(1). Edgar's paper showed that both approaches are equivalent, and the second approach is more computationally efficient.\n",
    "\n",
    "\n",
    "#### Bayesian hierarchical model\n",
    "\n",
    "$ p(\\bf y_t | \\bf y_{t-1}, \\boldsymbol \\theta, \\bf X_t, \\bf X_{t-1}, \\boldsymbol \\Phi_1, \\Sigma) = \\mathcal{N}(\\boldsymbol \\mu_t, \\Sigma + \\sigma_0^2 \\bf I)$\n",
    "\n",
    "$\\boldsymbol \\mu_t = \\bf X_t \\boldsymbol \\beta + \\boldsymbol \\Phi_1(\\bf y_{t-1} - \\bf X_{t-1} \\boldsymbol \\beta)$\n",
    "\n",
    "where\n",
    "\n",
    "$\\boldsymbol \\Sigma = Cov(v_i)\\ \\forall i = 1,...,T$ is the $S \\times S$ spatial covariance matrix, e.g., formulated by the exponential function with matrix entries of the form\n",
    "\n",
    "$\\boldsymbol \\Sigma_{ij} := C(h_{ij} | \\boldsymbol \\theta) = \\sigma_1^2 \\exp(-h_{ij}/\\alpha), \\ \\alpha > 0,\\ h_{ij}$ is the stream distance between locations $s_i$ and $s_j$. Denote spatial covariance parameters by $\\boldsymbol \\theta = (\\sigma_0^2,\\sigma_1^2,\\alpha)$.\n",
    "\n",
    "\n",
    "$\\boldsymbol \\Phi_1$ is the $S \\times S$ transition matrix determining the amount of temporal autocorrelation, e.g., in the simplest case, assuming the same temporal autocorrelation for all spatial locations, the diagonal elements of $\\boldsymbol \\Phi$ are all equal to $\\phi$ and all the off-diagonal ones are set to zero,\n",
    "\n",
    "\n",
    "$\\boldsymbol \\Phi_1 = \n",
    "\\begin{bmatrix}\n",
    "\\phi & 0 & 0 & \\cdots & 0 \\\\\n",
    "0 & \\phi & 0 & \\cdots & 0 \\\\\n",
    "0 & 0 & \\phi & \\cdots & 0 \\\\\n",
    "\\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
    "0 & 0 & 0 & \\cdots & \\phi \\\\\n",
    "\\end{bmatrix}\n",
    "$\n",
    "\n",
    "\n",
    "##### Hyper priors\n",
    "\n",
    "$\\beta_1,...,\\beta_p \\sim \\mathcal(0,10)$   # prior on the regression coefficients\n",
    "\n",
    "$\\sigma_0 \\sim \\text{Uniform}(0, 1) $ # prior on the nugget effect\n",
    "\n",
    "$\\sigma_1 \\sim \\text{Uniform}(0, 10) $ # prior partial sill parameters\n",
    "\n",
    "$\\alpha \\sim \\text{Uniform}(0, 16) $ # prior on spatial range parameters\n",
    "\n",
    "$\\phi \\sim \\text{Uniform}(-1,1) $ # prior on the autoregressive parameters, elements of $\\boldsymbol \\Phi_1$\n",
    "\n",
    "\n",
    "\n",
    "\\\n",
    "See: https://www.fs.usda.gov/rm/pubs_journals/2022/rmrs_2022_santos_fernandes_e001.pdf\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.geometry import Point\n",
    "import torch\n",
    "\n",
    "# sample points in continuous domain\n",
    "\n",
    "\n",
    "def random_sample_polygon(polygon, number):\n",
    "    points = []\n",
    "    minx, miny, maxx, maxy = polygon.bounds\n",
    "    while len(points) < number:\n",
    "        pnt = Point(np.random.uniform(minx, maxx), np.random.uniform(miny, maxy))\n",
    "        if polygon.contains(pnt):\n",
    "            points.append(pnt)\n",
    "    return points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<POINT (0.692 2.565)>, <POINT (0.546 0.734)>, <POINT (0.167 2.385)>]"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_t = random_sample_polygon(polygon, 3)\n",
    "s_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, dpi=90)\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "plot_polygon(polygon, ax=ax, add_points=False, color=\"b\")\n",
    "xs = [point.x for point in s_t]\n",
    "ys = [point.y for point in s_t]\n",
    "plt.scatter(xs, ys)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import scipy\n",
    "# h_ij = scipy.spatial.distance_matrix([(0, y) for y in ys], [(0, y) for y in ys])\n",
    "# h_ij\n",
    "\n",
    "# POTNEITALLY INCORPORATE small kernel term that involves x coordinate -> effect of being a gradient term that encouages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAP from continuous domain to geospaital xy coords\n",
    "def xy_segment_info(s_i, df):\n",
    "    # get corresponding segment of s_i from df\n",
    "    idx_df = [i for i in range(len(df)) if s_i.within(Polygon(df[\"box_coord\"].iloc[i]))]\n",
    "    row = df.iloc[idx_df].squeeze()\n",
    "\n",
    "    # Calculate the fraction of the total distance\n",
    "    dist_along_segment = row[\"upDist\"] - s_i.y\n",
    "\n",
    "    return {\n",
    "        \"pid_from\": row[\"pid_from\"],\n",
    "        \"pid_to\": row[\"pid_to\"],\n",
    "        \"dist_along_segment\": dist_along_segment,\n",
    "        \"length\": row[\"Length\"],\n",
    "    }\n",
    "\n",
    "\n",
    "def xy_along_segment(s_i, df):\n",
    "    # get corresponding segment of s_i from df\n",
    "    idx_df = [i for i in range(len(df)) if s_i.within(Polygon(df[\"box_coord\"].iloc[i]))]\n",
    "    row = df.iloc[idx_df].squeeze()\n",
    "\n",
    "    # get line start and end points on geographic space\n",
    "    x1, y1 = row[\"points\"][0]\n",
    "    x2, y2 = row[\"points\"][1]\n",
    "\n",
    "    # Calculate the differences in x and y coordinates\n",
    "    dx = x2 - x1\n",
    "    dy = y2 - y1\n",
    "\n",
    "    # Calculate the fraction of the total distance\n",
    "    dist_along_segment = row[\"upDist\"] - s_i.y\n",
    "    fraction = dist_along_segment / row[\"Length\"]\n",
    "\n",
    "    # Calculate the coordinates of the point along the line\n",
    "    x = x1 + fraction * dx\n",
    "    y = y1 + fraction * dy\n",
    "\n",
    "    return (x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.4789120703848688, 2.4598474200953406),\n",
       " (0.0, 0.7340528221804109),\n",
       " (0.12135822263676761, 2.3679383062603123)]"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_coords = [xy_along_segment(s_i, df) for s_i in s_t]\n",
    "geo_coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'pid_from': 7,\n",
       "  'pid_to': 4,\n",
       "  'dist_along_segment': 0.5074231685794306,\n",
       "  'length': 1.0921406484209},\n",
       " {'pid_from': 2,\n",
       "  'pid_to': 1,\n",
       "  'dist_along_segment': 0.2659471778195891,\n",
       "  'length': 1.0},\n",
       " {'pid_from': 3,\n",
       "  'pid_to': 4,\n",
       "  'dist_along_segment': 0.6481264033783898,\n",
       "  'length': 1.052507581049577}]"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "si_info = [xy_segment_info(s_i, df) for s_i in s_t]\n",
    "si_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "# encode as networkx object and plot\n",
    "G = nx.Graph()\n",
    "G.add_weighted_edges_from(\n",
    "    list(zip(shapefile.pid_from, shapefile.pid_to, shapefile.Length))\n",
    ")\n",
    "\n",
    "# encode as networkx object and plot, dorected graph\n",
    "DiG = nx.DiGraph()\n",
    "DiG.add_weighted_edges_from(\n",
    "    list(zip(shapefile.pid_from, shapefile.pid_to, shapefile.Length))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pid_variables_from_idx(point_idx, si_info):\n",
    "    return si_info[point_idx][\"pid_from\"], si_info[point_idx][\"pid_to\"]\n",
    "\n",
    "\n",
    "def is_flow_connected_directed(pid_from_1, pid_from_2):\n",
    "    try:\n",
    "        nx.shortest_path_length(\n",
    "            DiG, source=pid_from_1, target=pid_from_2, weight=\"weight\"\n",
    "        )\n",
    "        return True\n",
    "    except nx.NetworkXNoPath:\n",
    "        return False\n",
    "\n",
    "\n",
    "def is_flow_connected(pid_from_1, pid_from_2):\n",
    "    return any(\n",
    "        [\n",
    "            is_flow_connected_directed(pid_from_1, pid_from_2),\n",
    "            is_flow_connected_directed(pid_from_2, pid_from_1),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "\n",
    "def get_stream_dist(point_idxs):\n",
    "    if is_flow_connected_directed(\n",
    "        si_info[point_idxs[1]][\"pid_from\"], si_info[point_idxs[0]][\"pid_from\"]\n",
    "    ):\n",
    "        # if flow connected, reverse order so that we enforce pt 1 to be upstream of pt 2\n",
    "        point_idxs = point_idxs[::-1]\n",
    "\n",
    "    pid_from_1, pid_to_1 = pid_variables_from_idx(point_idxs[0], si_info)\n",
    "    pid_from_2, pid_to_2 = pid_variables_from_idx(point_idxs[1], si_info)\n",
    "\n",
    "    if is_flow_connected(pid_from_1, pid_from_2):\n",
    "        # get in_between_dist, now pt 1 is guaranteed to be most upstream\n",
    "        in_between_dist = nx.shortest_path_length(\n",
    "            DiG, source=pid_from_1, target=pid_to_2, weight=\"weight\"\n",
    "        )\n",
    "        # upstream s_i point, get distance from pid_to up to the s_i point along segment\n",
    "        dist_top_to_pt_1 = si_info[point_idxs[0]][\"dist_along_segment\"]\n",
    "\n",
    "        # downstream s_i point, get distance from pid_from doen to the s_i point along segment\n",
    "        dist_bottom_to_pt_2 = (\n",
    "            si_info[point_idxs[1]][\"length\"]\n",
    "            - si_info[point_idxs[1]][\"dist_along_segment\"]\n",
    "        )\n",
    "        stream_dist = in_between_dist - dist_top_to_pt_1 - dist_bottom_to_pt_2\n",
    "\n",
    "    else:\n",
    "        in_between_dist = nx.shortest_path_length(\n",
    "            G, source=pid_from_1, target=pid_from_2, weight=\"weight\"\n",
    "        )\n",
    "        dist_top_to_pt_1 = si_info[point_idxs[0]][\"dist_along_segment\"]\n",
    "        dist_top_to_pt_2 = si_info[point_idxs[1]][\"dist_along_segment\"]\n",
    "        stream_dist = in_between_dist - dist_top_to_pt_1 - dist_top_to_pt_2\n",
    "\n",
    "    return stream_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 1.83080535, 0.98909866],\n",
       "       [1.83080535, 0.        , 1.65046905],\n",
       "       [0.98909866, 1.65046905, 0.        ]])"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_ij = np.array([[get_stream_dist([i, j]) for j in range(3)] for i in range(3)])\n",
    "h_ij"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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muaCgOYDC1wvr5o6AlweDHwrU+Mfxq0IXSVDW4VhnSm9DW++8Gr68ug4fZlk2llw+fTAUPl3fa9pZKGgOkqAKtC3E+c6eCy6xWI2zqIJ8ERXiD5OZxZHLzpmjxrIs3rxr1vTMBP5nTXNBQXOgZ5L6YVqcEo0mFou+yIW2rvu215w9GTTzvAZZtlnTQwSZNc0FBc2BGIbBn341DH2CfHH9Tj3+95ufu217zTpK5PClSpgdvOZjnbEJK7+zrOWy8MH+iAoJcOjnOwMFzcHkMi98MncEvD0kyDyvwd+OlghdJEGM6hsEf6knqmqNyHfwY48Ps4pw/U49egf64MWHhJs1zQUFzQnieivwevMcqNU/XMSZ0tsCl4h/3p4SPBht2drJkb2PlzU12HSkGIDws6a5oKA5ybyxffCLYWFoMrN4cWsubutdb8eVrrL2Pjpqday7Z00nDw5Fcqw49trrDAqakzAMg/QnhyIy2A83tA1Y9nWew9sqYmdd3uDna1rcrOnc/gwd2Xn2On4qqYbMS4I3Hxd+1jQXFDQnCpB5Yd3TIyD1lOBg4U18ml0sdJF4FSKXIa63ZQXfw5e61vuorWvEO9+La9Y0FxQ0J4sNl2Pl40MAAO/tL8TJEvGsEsWHh2IcszPoX/ZbZk1HhfhjwQP9HVE0XlHQeDB7tApPDO8Nk5nFi1/moqq267dRrmJSczd/9uWbaDTZt+bjz+V3bLMj3popnlnTXLheiV0QwzB4OyUOA3r5QaMz4I9fdZ/2WnxEIIL8vFHT0GRX76tl1nSBbda0dcCyq6Gg8cRP6olP5o6EzEuCI5ersO5gx7ueugsPCYNJzVs72TOa/4ufSpF/XYsAEc6a5oKCxqMYZQDeThkKAHj/x0uCTPcXgvX2kWs7rbKmAX/Za5k1/fLUGNHNmuaCgsazX42MwKyRETCzwB++zENljTi3OnKkidG9IGGAS5paXLvd+TUf37171vRY+zZ0FwsKmgBWzYxDTGgAqmoNWPJlnsvs/2wvha8XRtrWfOxcN//xK1XIaJ41/XZKnChnTXPBKWjr16/HsGHDIJfLIZfLkZiYiB9++KHDc7Zv345BgwZBJpNh6NCh2LNnT5cK7A58vD2wbu4I+Hp7IKf4FtY2z6lyZ5M53D4am8x4PaNl1rR1/zVXxiloERERWL16Nc6cOYPTp0/joYcewsyZM3Hu3Lk2jz9+/DjmzJmD5557DmfPnkVKSgpSUlJQUEC7skSF+CP9SUt77aMDl5HdxQe6YmcdjnX8ShUaGjte8/GzI8W4clOPYH9vUc+a5oTtoh49erCbNm1q83tPPfUUO3369FbvjR07lv3973/P6RparZYFwGq1WrvLKVYvfZ3Hjnv3R/bDHy8JXRSnMpvN7Lh3f2T7vrKbPXBR0+5xZbf0bMzyPWzfV3azO3LLeSwhd1x+L+1uo5lMJmzbtg16vR6JiYltHpOTk4Pk5ORW702dOhU5OTkdfrbBYIBOp2v1clcLH4xEhbYB6w9fceu5awzDYJJ1kHEHt48rvzuHhkYzxvUPQooDNnQXC85By8/Ph7+/P6RSKZ5//nns3LkTsbFtD/BUq9UIDW09wjo0NBRqtbrDa6Snp0OhUNheKpWKazFdRt+efgCAOqMJunr3XvHYtmhPYWWbf1Qyz2vw4wXLrOm3Bdpr2lk4By0mJgZ5eXn46aef8MILL2D+/Pk4f/68QwuVlpYGrVZre5WXu+9mEjIvD/TwtSwqU6GrF7g0zpU0oCe8PSQor67HlZv6Vt+rMzbhzea9pl1l1jQXnIPm7e2NqKgojBw5Eunp6YiPj8fatWvbPFapVEKj0bR6T6PRQKlUdngNqVRq69m0vtxZmMIHAFChde9nan5ST4ztHwTg3t7Hjw643qxpLrr8HM1sNsNgaHuQbGJiIrKyslq9l5mZ2W6brrsKU8gAABV33DtoQNs7g17W1OCz5ilEb7rQrGkuOAUtLS0N2dnZuHr1KvLz85GWloZDhw5h7ty5AIDU1FSkpaXZjl+yZAn27t2LNWvW4OLFi3jzzTdx+vRpLF682LE/hYtTNgdNrXXvW0egpZ12sqQaNQ2N98yaftiFZk1z4cnl4MrKSqSmpqKiogIKhQLDhg3Dvn378PDDDwMAysrKIJG0ZDcpKQlbt27F8uXL8dprryE6OhoZGRmIi4tz7E/h4sIDu8etIwD0C/ZDZLAfSqr0OFZUhTqjyTZrWgx7TTsLp6D97W9/6/D7hw4duue9WbNmYdasWZwK1d0o5c23jt0gaIDl9rGkqgR7C9Q4WmQZWP2HKdFQBbnWrGkuaKyjCNjaaN3g1hFo2dpp7zk1qmpdd9Y0FxQ0EVAqWmo0d35obTUmMghSTwkaGi0zrl111jQXnG4diXNYu/frjCboGppEvVmDI3gwDAJ9veApkWBYhMJlZ01z4d5/RlyEj7cHApsfWqu7QTvt82Ml0OgMqDU04a2ZQ4QuDi8oaCLR0iHi3u208uo6/DXzEgDg/6YPRnCATOAS8YOCJhLdoYufZVm8tjPfNmh41sgIoYvEGwqaSNzdIeKuduXdwJHLVfD2lCD9yWFuNWj4fihoIhEmd+/RIbf1RqzabRl8vmRKNCKD/QQuEb8oaCIR5ua3jm9/fwHVeiNiQgOw8EH3fmbWFgqaSIS58a3jsaIqfJt7DQwDvPvkULd/ZtaW7vcTi1TLwGL3ClpDowmv7cwHYFlox7oaVndDQRMJa41Wa2iCrsF99r5em3UZpbfqoJTL8P+muslCO3agoImEr7enbUSIu9RqFyp02Ng8z2zVzCEIkLn3iJeOUNBExJ3aaSYzi1d35MNkZvHoECUeGdLxrHp3R0ETkZaZ1q7fxf+vnKv4ufwOAqSeWNlNhll1hIImIko3WTvkxp16/GWfZXOKV6YNQqi8ewyz6ggFTUTC3KDnkWVZvJ5RAL3RhFF9e+DpMX2ELpIoUNBExBq0Gy48OuSHAjWyLlrWZkx/cigkLr45haNQ0ETEOi/NVWs0bV0jVjSvzfjCpChEh7rX2oxdQUETEVd/aL1670XcrDGgfy8//M+kAUIXR1QoaCJivXWsMTShxsUeWp8sqcaXJy0buqc/MRQyL/dbm7ErKGgi4if1hFxmWV3ClWo1Q5MJaTv+AwCYM0aFsf3df2kCrihoIuOKy4N/cvBK835mUrz6qOtu6O5MFDSRCQt0rSUNiipr8MmhIgDAm4/HQuHbfYdZdYSCJjKuNAzLbGaRtiMfjSYWUwaFYPrQMKGLJFoUNJFRyl2ni//LU2U4dfU2fL09sMrN9jNzNAqayFhvHW+IPGgaXQNW77kIAPjfR2LQu3mGOGkbBU1kwlxkZ5mV351DjaEJ8REKzE/qJ3RxRI+CJjKu0EbLPK/Bnnw1PCQM0p8cBg8aZnVfFDSRsY7gr2loQq1BfHta1zQ04vWMAgCWLXBjw917N1ZHoaCJjL/UEwG2h9biu31cs/8S1LoG9AnyxZIp7rcFrrNQ0ERIrLePuWW38Y+cqwCAd58Y6pZb4DoLBU2EbBNARbSndaPJjLRv88GywJMjeuOB6GChi+RSOAUtPT0do0ePRkBAAEJCQpCSkoLCwsIOz9m8eTMYhmn1ksloxm1HwkVYo23MLkahpgY9fL2wfLr7boHrLJyCdvjwYSxatAgnTpxAZmYmGhsb8cgjj0Cv13d4nlwuR0VFhe1VWlrapUK7O9t0GZ042mglVXqszboMAHj9F7EI8vMWuESuh9NGhHv37m319ebNmxESEoIzZ85gwoQJ7Z7HMAyUyu69ChIXtpnWAt46mswsTpZUQ6Orx2dHSmBsMuPB6GA8Mby3YGVyZV3a8VOr1QIAgoKCOjyutrYWffv2hdlsxogRI/Duu+9iyJD2V0YyGAwwGAy2r3U6XVeK6XKEnmm9t6ACK787f8+t6yOxoTTMyk52d4aYzWYsXboU48ePR1xcXLvHxcTE4PPPP8euXbuwZcsWmM1mJCUl4dq1a+2ek56eDoVCYXupVCp7i+mShNw8fm9BBV7Ykttm+/CNXeewt6CC9zK5A4a1c3fyF154AT/88AOOHj2KiIjObyjX2NiIwYMHY86cOXjrrbfaPKatGk2lUkGr1UIud/8HpDUNjRj65n4AwLmVU+En5WercZOZxQN/OtBuJwwDS/vx6CsP0WgQWH4vFQpFp34v7arRFi9ejN27d+PgwYOcQgYAXl5eGD58OIqKito9RiqVQi6Xt3p1JwEyLwQ0h4vPnseTJdUdXo9tLs/JkmreyuQuOAWNZVksXrwYO3fuxIEDBxAZGcn5giaTCfn5+QgLo7lLHRFioZ7Kms5dq7PHkRacgrZo0SJs2bIFW7duRUBAANRqNdRqNerrW9oSqampSEtLs329atUq7N+/H8XFxcjNzcW8efNQWlqKBQsWOO6ncENKAdZ4DOnkxu2dPY604HTzv379egDApEmTWr3/97//Hc888wwAoKysDBJJS35v376NhQsXQq1Wo0ePHhg5ciSOHz+O2Fh66NmRcAF6HsdEBiHY3xtVtcY2v29to42J7LiXmdyLU9A6029y6NChVl+///77eP/99zkVigChcikA4ETxLYzuF4QxkUFO74BgWRa+7YxftF55xYxY6gixAz/dWYSTvQUVtsG7x6/cwvErtxCmkGHFjFg8Gue8tu3m41dRVl0PX28P+Es9UVnT0vOr5OH67oyCJjLW51j/fe+g1jbghS25WD9vhFN+2cur67Bm/yUAllrrVyNVOFlSjcqaBoQEyHipUd0ZBU1ETGYWK787f0/IAEvXOgNg5Xfn8XCs0qG/9CzLYnlGAeobTRgbGYSnRqnAMAwSB9BCqI5C02RERKjnWP/++QYOX7oJb08J3n1yKA2zcgIKmogI8Rzrtt6IVd+dBwC8ODkKA3r5O+yzSQsKmogI8Rzr3T0XcEtvRHSIP34/kXaAcRYKmoiMiQxCmEKG9m7cGFgGHDvqOdbxoipsP2MZ3L36l0Ph7Um/Ds5C/7Ii4iFhsGKG5UH+f4fN0c+xGhpNeG1nPgBg3rg+GNmXHkI7EwVNZB6NC8P6eSNsQ7CslAqZQ7v2PzpwGVdv1SFULsXLjw5yyGeS9lH3vgg9GheGh2OVSP/hAjYdKUGCKhDfvpDksC79i2odPj1cDABY+Xgc5DLaAcbZqEYTKQ8Jg/FRlpWmGhpNDguZyczi1W/z0WRm8UhsKB6NoyUm+EBBEzHbwGKd47rzt5woRV75HfhLPbFqZvsz44ljUdBEzNpOu1PXiHqjqcufd+NOPf6817IDzCuPxtzTDiTOQ0ETMbnM0zaavqvrh7Asizd2nYPeaMKIPoGYO7avI4pIOomCJmIMw9y1jVPXbh/3Fqjx4wUNvDwsO8BIaIAwryhoImddeq4rGxNq6xux4t/nAADPTxyAGGWAQ8pGOo+CJnJKB2xM+Oe9F1FZY0BksB8WTY5yVNEIBxQ0kevqOvynrlbji5/KAFh2gJF50Q4wQqCgiZxtZxk7gmZoMiFth2WY1exRKppfJiAKmsh1Za+0DYeKUVRZi2B/b6Q9RsOshERBE7mwQPvaaEWVNVh30LJI7RszhiDQl3aAERIFTeTC5JZbx9scHlqbzSxe21EAo8mMSTG9MGMYLagjNAqayMl9POHT3IHR2aFYX50ux8mr1fDx8sDbKXG0NIEIUNBEjmEY2+1jZ0aHVOoa8O6eCwCAlx4ZiIgevk4tH+kcCpoLsHWIdGJjwpXfnUdNQxOG9lbgmaR+Ti4Z6SwKmgtQyjs3iv/H8xp8n18BDwmD9CeHwtOD/nvFgv4nXEB4J24daw1NeGNXAQBgwQORiOut4KVspHMoaC5A2Ylbx/f2FeKGtgGqIB8sSY7mq2ikkyhoLuB+D63zyu/Y1up/J2UofL1phQqxoaC5gLAOZlo3msx49dv/gGWBJ4b3xoSBvfguHukECpoLsNZo1XojGhpbP7T+7EgxLqprEOjrheXTBwtRPNIJFDQXoPDxgszL8l919wTQq1V6rP3xMgBg+fRY9PSXClI+cn8UNBfAMIxtoR5rO41lWfxfRj4MTWaMj+qJX47oLWQRyX1wClp6ejpGjx6NgIAAhISEICUlBYWFhfc9b/v27Rg0aBBkMhmGDh2KPXv22F3g7srW89jcxb8j9zqOFd2C1FOCd1JoBxix4xS0w4cPY9GiRThx4gQyMzPR2NiIRx55BHq9vt1zjh8/jjlz5uC5557D2bNnkZKSgpSUFBQUFHS58N2J8q6ex1u1Brz9vWUHmCXJ0egX7Cdk0UgnMGxnNqZux82bNxESEoLDhw9jwoQJbR4ze/Zs6PV67N692/beuHHjkJCQgA0bNnTqOjqdDgqFAlqtFnK53N7iurT39hXi44NF+M24vqg1NGHn2esYpAzAdy8+AC8aASIILr+XXfof0mq1AICgoPY3SMjJyUFycnKr96ZOnYqcnJx2zzEYDNDpdK1e3Z21Riu4fgc7z14HwwCrfzmMQuYi7P5fMpvNWLp0KcaPH4+4uPZXvFWr1QgNDW31XmhoKNRqdbvnpKenQ6FQ2F4qlcreYroNaxd/wQ3LH535if2QoAoUsESEC7uDtmjRIhQUFGDbtm2OLA8AIC0tDVqt1vYqLy93+DVcjbVGazSxCFfI8L9TYwQuEeHCrrE6ixcvxu7du5GdnY2IiIgOj1UqldBoNK3e02g0UCrb31xBKpVCKqVnQnfr4euN3goZqvRGrHw8Fv5SGmblSjjVaCzLYvHixdi5cycOHDiAyMjI+56TmJiIrKysVu9lZmYiMTGRW0m7ObWuAde1DQj08cLDQ2hpAlfD6c/iokWLsHXrVuzatQsBAQG2dpZCoYCPj+WBampqKnr37o309HQAwJIlSzBx4kSsWbMG06dPx7Zt23D69Gls3LjRwT+Ke7ukrgEADKRVhl0Spxpt/fr10Gq1mDRpEsLCwmyvr776ynZMWVkZKioqbF8nJSVh69at2LhxI+Lj4/HNN98gIyOjww4Ucq9CjSVoMaEUNFfEqUbrzCO3Q4cO3fPerFmzMGvWLC6XIv/lkoZqNFdGD2FcRKG6FgDVaK6KguYCbtUaUFVrAABEh/oLXBpiDwqaC7iksdRmfYJ8afa0i6KguQBb+4xuG10WBc0F2Hoclc65bbx2uw4HCys71dlF7ENBcwG2Z2hOqtE+PlCEZ/9+Cqt2n3fK5xMKmuixLHtXjeb4oJVX1+GbM9cAAL+gzTCchoImcmpdA2oamuApYdA/2PG3jusPX0GTmcX4qJ4Y2bf96U6kayhoImftcYwM9oO3p2P/u67fqcf205aZEUumDHToZ5PWKGgi58wxjusPFaHRxCKxf0+MiaTazJkoaCLnrDGOFdp6fH3K0jajJcSdj4Imcs56hrbh0BUYTWaMiQzCuP60ibyzUdBEzGxmbUFzZI+jRteAL09Z2mZLp1BtxgcKmoiV365DQ6MZUk8J+gQ5bufO9YeuwNhkxuh+PZA4gGozPlDQRKywuSMkKsQfHhLHLJBaqWvAlyfLAAB/mBJNC6/yhIImYpec0BHyaXYxDE1mjOgTiAeigh32uaRjFDQRK2x+huaorv2bNQZ88VMpAGBJ8kCqzXhEQRMx6zM0R9VoG7OvoKHRjARVICZEU23GJwqaSBmbzLhy03E1WlWtAVtOWNpmS6htxjsKmkhdvaVHk5mFv9QT4c2Lp3bFZ0eKUd9owrAIBSbF0K6gfKOgiVShbWqMf5drn2q9Ef/KaW6bUW0mCAqaSDnyQfWmI8WoM5oQ11uOhwaFdPnzCHcUNJEqdNBkz9t6I/5x/CoA4A8PUW0mFAqaSDnqGdrfjpZAbzQhNkyOh2ND738CcQoKmgjVG00ora4D0LUexzt1Rmy21mbUNhMUBU2EiiprwbJATz9vBPvbv6vO58euotbQhEHKADxCtZmgKGgiVOiAqTHa+kb8/VgJAEttJnHQWEliHwqaCDmix/Hvx0pQ09CEgaH+eHRI+3vREX5Q0ESoqz2OuoZGfH7UUpu9+BDVZmJAQROhy5qWh9X2+Mexq9A1NCEqxB+PDaUl5MSAgiYyuoZG3NA2AACi7ajRahoasclWm0U5bB4b6RoKmshYa7MwhQwKHy/O5/8zpxTa+kb07+WHXwwLd3TxiJ0oaCJj3QfNnvZZraEJnx0pBkC1mdhwDlp2djZmzJiB8PBwMAyDjIyMDo8/dOgQGIa552Xd/5q01pUex3/llOJOXSMig/0wg2ozUeEcNL1ej/j4eKxbt47TeYWFhaioqLC9QkJocGtb7O1x1N9Vmy2eHAVPD7pZERPOu9pNmzYN06ZN43yhkJAQBAYGcj6vu7F3jOOWE6Wo1hvRt6cvZiZQbSY2vP3ZS0hIQFhYGB5++GEcO3asw2MNBgN0Ol2rV3dQVWvALb0RDGNZ+aqz6o0mbMy21GaLqDYTJaf/j4SFhWHDhg349ttv8e2330KlUmHSpEnIzc1t95z09HQoFArbS6VSObuYomBdI6RvkC98vD06fd4XP5Xilt4IVZAPnhje21nFI13g9A2RY2JiEBMTY/s6KSkJV65cwfvvv49//etfbZ6TlpaGZcuW2b7W6XTdImz2jHGsN5qw4XBL28yLajNREmTn8TFjxuDo0aPtfl8qlUIqtX/Uuquyp8fxy5NlqKo1oHegD54YHuGsopEuEuTPX15eHsLCaGjQf+Pa49jQaMKGw1cAWNpmjt4/jTgO5xqttrYWRUVFtq9LSkqQl5eHoKAg9OnTB2lpabh+/Tr++c9/AgA++OADREZGYsiQIWhoaMCmTZtw4MAB7N+/33E/hRtgWda26WBna7RtJ8tQWWOpzX41kmozMeMctNOnT2Py5Mm2r61tqfnz52Pz5s2oqKhAWVmZ7ftGoxEvvfQSrl+/Dl9fXwwbNgw//vhjq88gwA1tA2oNTfDyYNCvp999j29oNGF9c232wqQBVJuJHOegTZo0CSzLtvv9zZs3t/r65Zdfxssvv8y5YN2Ntcexf7B/p0Kz/XQ5NDoDwhQyzBpFtZnY0Z9BkbD1OHbittHQZMInh1pqM6ln5x8FEGFQ0ESiZZ39+z+o3n76Giq0DQiVS/HUKPd/7OEOKGgiYa3R7jcHzdhkxvrm2uz5iQMg86LazBVQ0ETAZGZxubK5x/E+Qfs29xqu36lHrwAp5ozpw0fxiANQ0ESg9JYexiYzZF4SqDrYQrfRZMa6g5ZHK1SbuRYKmghYR4REhwR0OFlzR+41XLtdj2B/KZ6m2sylUNBEwPqguqMRIY0mMz5urs1+P6E/p0HHRHgUNBEotI1xbL/HMePsdZRX16OnnzfmjqPazNVQ0ETg0n3GODbdVZv9bkJ/+HoLMhacdAEFTWCGJhNKqvQA2h/j+O+fb6D0Vh2C/Lwxb1xfPotHHISCJrCSKssWugEyTyjl926hazKz+PiApTZb8GAk/KRUm7kiCprACtUta4S0ta3Sdz/fQHGVHoG+XkhN7Mdz6YijUNAEdqmDMY4mM4sPD1wGACx8sD/8qTZzWRQ0gVkXTG1rRMj3+RUovqmHwscLqYnUNnNlFDSBXWpnnRCzmcVHWZba7LkHIhEg4748OBEPCpqA6oxNKLNuoftfo/b3FFTgcmUtAmSeeGZ8PwFKRxyJgiagy80jQoL9peh51xa6ltrM0tP42/GRkFNt5vIoaAJqb0TIvnNqFGpqECD1xG/HRwpRNOJgFDQBWUeERIe0tM/MZhZrm9tmz47vB4Uv1WbugIImoMI21nHcf16Di+oa+Es98dsHqDZzFxQ0Af13jyPLsviwuTabn9QXgb7egpWNOBYFTSB36ozQ6AwAWnocf7xQifMVOvh5e2DBA/2FLB5xMAqaQKxz0HoH+iBA5gWWZbE26xIAIDWpH3r4UW3mTihoAmnZ0MJSmx24WImC6zr4eHlgAbXN3A4FTSC2OWjKgFZts9TEvq2eqRH3QEETSOFdO3seunQTP1/TwsfLAwsnUNvMHVHQBMCyLC7fdeu49kdLbTZvXB8EU23mlihoArhZa8DtukZIGECtNSCv/A6knhKqzdwYBU0Al5qnxvTt6WfbEWbu2L4ICbh3hjVxDxQ0AVjbZ8H+3jhTehvenhI8P5FqM3dGQROAtcfx+u16AMDTY/ogpI31Qoj7oKAJwFqj3dA2wNtDgucnDhC4RMTZKGg8M5tbehwB4NdjVFAqqDZzd5yDlp2djRkzZiA8PBwMwyAjI+O+5xw6dAgjRoyAVCpFVFTUPbuCdifX79RDbzQBALwkDF6YRLVZd8A5aHq9HvHx8Vi3bl2nji8pKcH06dMxefJk5OXlYenSpViwYAH27dvHubDu4NJdtdlTo1UIU/gIWBrCF87rl02bNg3Tpk3r9PEbNmxAZGQk1qxZAwAYPHgwjh49ivfffx9Tp07lenmXl3WxEgDAMMD/TI4SuDSEL05vo+Xk5CA5ObnVe1OnTkVOTk675xgMBuh0ulYvd5HYvycGhvrjgQHB6B1ItVl34fSgqdVqhIaGtnovNDQUOp0O9fX1bZ6Tnp4OhUJhe6lU7rFPc52xCS9+eRaXNLXY8JsRQheH8EiUvY5paWnQarW2V3l5udBFcri2lv8m7svpa0wrlUpoNJpW72k0Gsjlcvj4tH3rJJVKIZXS4FriPpxeoyUmJiIrK6vVe5mZmUhMTHT2pQkRDc5Bq62tRV5eHvLy8gBYuu/z8vJQVlYGwHLbl5qaajv++eefR3FxMV5++WVcvHgRn3zyCb7++mv88Y9/dMxPQIgL4By006dPY/jw4Rg+fDgAYNmyZRg+fDjeeOMNAEBFRYUtdAAQGRmJ77//HpmZmYiPj8eaNWuwadOmbtm1T7ovzm20SZMmgWXZdr/f1qiPSZMm4ezZs1wvRYjbEGWvIyHuhoJGCA8oaITwgIJGCA8oaITwgIJGCA8oaITwgIJGCA8oaITwgIJGCA8oaITwgILGI5O5ZYzoT8XVrb4m7o2CxpO9BRVI/uth29fPbj6FB/50AHsLKgQsFeELBY0Hewsq8MKWXNue1VZqbQNe2JJLYesGKGhOZjKzWPndebR1k2h9b+V35+k20s1R0JzsZEk1KrQN7X6fBVChbcDJkmr+CkV4R0Fzssqa9kNmz3HENVHQnKyzmwvSJoTujYLmZGMigxCmkKG9VRwZAGEKGcZEBvFZLMIzCpqTeUgYrJgR2+b3rOFbMSMWHhJaUNWdUdB48GhcGNbPG4FQeetFYZUKGdbPG4FH48IEKhnhi9NXKiYWj8aFYXxUMIa+uR8A8PdnRmPCwF5Uk3UTVKPx6O5Qje0fRCHrRihohPCAgkYIDyhohPCAgkYIDyhohPCAgkYIDyhohPCAgkYIDyhohPCAgkYID+wK2rp169CvXz/IZDKMHTsWJ0+ebPfYzZs3g2GYVi+ZjOZeke6Fc9C++uorLFu2DCtWrEBubi7i4+MxdepUVFZWtnuOXC5HRUWF7VVaWtqlQhPiajgH7a9//SsWLlyIZ599FrGxsdiwYQN8fX3x+eeft3sOwzBQKpW2V2hoaJcKTYir4RQ0o9GIM2fOIDk5ueUDJBIkJycjJyen3fNqa2vRt29fqFQqzJw5E+fOnevwOgaDATqdrtWLEFfGKWhVVVUwmUz31EihoaFQq9VtnhMTE4PPP/8cu3btwpYtW2A2m5GUlIRr1661e5309HQoFArbS6VScSkmIaLj9F7HxMREpKamIiEhARMnTsSOHTvQq1cvfPrpp+2ek5aWBq1Wa3uVl5c7u5iEOBWnGdbBwcHw8PCARqNp9b5Go4FSqezUZ3h5eWH48OEoKipq9xipVAqpVNru9wlxNZxqNG9vb4wcORJZWVm298xmM7KyspCYmNipzzCZTMjPz0dYGK2TQboPzmuGLFu2DPPnz8eoUaMwZswYfPDBB9Dr9Xj22WcBAKmpqejduzfS09MBAKtWrcK4ceMQFRWFO3fu4C9/+QtKS0uxYMECx/4khIgY56DNnj0bN2/exBtvvAG1Wo2EhATs3bvX1kFSVlYGiaSlorx9+zYWLlwItVqNHj16YOTIkTh+/DhiY9tego0Qd8SwLCv63RV0Oh0UCgW0Wi3kcrnQxbFbnbEJsW/sAwCcXzUVvt60CJkr4/J7SWMdCeEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOEBBY0QHlDQCOGBXUFbt24d+vXrB5lMhrFjx+LkyZMdHr99+3YMGjQIMpkMQ4cOxZ49e+wqLCGuinPQvvrqKyxbtgwrVqxAbm4u4uPjMXXqVFRWVrZ5/PHjxzFnzhw899xzOHv2LFJSUpCSkoKCgoIuF54QV8F5D+uxY8di9OjR+PjjjwEAZrMZKpUKL774Il599dV7jp89ezb0ej12795te2/cuHFISEjAhg0b2ryGwWCAwWCwfa3T6aBSqWgPayIqTtvD2mg04syZM0hOTm75AIkEycnJyMnJafOcnJycVscDwNSpU9s9HgDS09OhUChsL5VKxaWYouXr7Ymrq6fj6urpFLJuhlPQqqqqYDKZEBoa2ur90NBQqNXqNs9Rq9WcjgeAtLQ0aLVa26u8vJxLMQkRHVH+WZVKpZBKpUIXgxCH4VSjBQcHw8PDAxqNptX7Go0GSqWyzXOUSiWn4wlxR5yC5u3tjZEjRyIrK8v2ntlsRlZWFhITE9s8JzExsdXxAJCZmdnu8YS4JZajbdu2sVKplN28eTN7/vx59ne/+x0bGBjIqtVqlmVZ9je/+Q376quv2o4/duwY6+npyb733nvshQsX2BUrVrBeXl5sfn5+p6+p1WpZAKxWq+VaXEKchsvvJec22uzZs3Hz5k288cYbUKvVSEhIwN69e20dHmVlZZBIWirKpKQkbN26FcuXL8drr72G6OhoZGRkIC4uzlF/KwgRPc7P0YTA5XkFIXxx2nM0Qoh9KGiE8ICCRggPKGiE8ICCRggPKGiE8ICCRggPKGiE8ECUo/f/m/WZuk6nE7gkhLSw/j52ZsyHSwStpqYGANxmAihxLzU1NVAoFB0e4xJDsMxmM27cuIGAgAAwDCN0cbrEuixDeXk5DSdzAj7/fVmWRU1NDcLDw1uN722LS9RoEokEERERQhfDoeRyOQXNifj6971fTWZFnSGE8ICCRggPKGg8k0qlWLFiBa2J4iRi/fd1ic4QQlwd1WiE8ICCRggPKGiE8ICCRggPKGiE8ICCxjOue8uRzsnOzsaMGTMQHh4OhmGQkZEhdJFaoaDxiOvecqTz9Ho94uPjsW7dOqGL0iZ6jsYjrnvLEfswDIOdO3ciJSVF6KLYUI3GE3v2liPug4LGE3v2liPug4JGCA8oaDyxZ2854j4oaDyxZ2854j5cYoa1u1i2bBnmz5+PUaNGYcyYMfjggw+g1+vx7LPPCl00l1dbW4uioiLb1yUlJcjLy0NQUBD69OkjYMmaOWuTNtK2jz76iO3Tpw/r7e3Njhkzhj1x4oTQRXILBw8eZAHc85o/f77QRWNZlmXpORohPKA2GiE8oKARwgMKGiE8oKARwgMKGiE8oKARwgMKGiE8oKARwgMKGiE8oKARwgMKGiE8+P+iBsVkjirX4gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = shapefile.plot()\n",
    "plt.scatter([i[0] for i in geo_coords], [i[1] for i in geo_coords])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gstools import SRF, Gaussian\n",
    "\n",
    "# get bounding box for geographic space\n",
    "flatten_tuples = [t for tuples_list in df[\"points\"] for t in tuples_list]\n",
    "x_values, y_values = zip(*flatten_tuples)\n",
    "\n",
    "# create reference covariates\n",
    "model = Gaussian(dim=2, var=1, len_scale=1)\n",
    "srf = SRF(model, seed=10)\n",
    "\n",
    "x_field = np.arange(min(x_values), max(x_values), 0.01)\n",
    "y_field = np.arange(min(y_values), max(y_values), 0.01)\n",
    "\n",
    "field = srf.structured([x_field, y_field])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.imshow(\n",
    "    field.T,\n",
    "    origin=\"lower\",\n",
    "    extent=(min(x_values), max(x_values), min(y_values), max(y_values)),\n",
    ")\n",
    "plt.scatter([i[0] for i in geo_coords], [i[1] for i in geo_coords])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# implement model -- set parameters\n",
    "T = 10\n",
    "S = 3\n",
    "p = 1\n",
    "\n",
    "params = {\n",
    "    \"beta_0\": 2.0,\n",
    "    \"beta_1\": 1.0,\n",
    "    \"sigma_0\": 0.02,\n",
    "    \"sigma_1\": 0.25,\n",
    "    \"alpha\": 1.75,\n",
    "    \"phi\": 0.5,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.6169303 ,  0.31363873, -0.26148306])"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample from covariate values\n",
    "covariates = np.array([float(srf.structured([x, y]).squeeze()) for x, y in geo_coords])\n",
    "covariates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.0000, -0.6169],\n",
       "        [ 1.0000,  0.3136],\n",
       "        [ 1.0000, -0.2615]])"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_1 = torch.stack([torch.tensor(np.ones(S)), torch.tensor(covariates)]).t().float()\n",
    "X_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([2., 1.])"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta = torch.tensor([params[\"beta_0\"], params[\"beta_1\"]])\n",
    "beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.3831, 2.3136, 1.7385])"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean response\n",
    "X_t_beta = torch.matmul(X_1, beta)\n",
    "X_t_beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.0625, 0.0269, 0.0306],\n",
       "        [0.0269, 0.0625, 0.0132],\n",
       "        [0.0306, 0.0132, 0.0625]], dtype=torch.float64)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# zero mean random variable, spatially dependent\n",
    "SIGMA = (params[\"sigma_1\"] ** 2) * np.exp(-h_ij / params[\"alpha\"])\n",
    "SIGMA = torch.tensor(SIGMA)\n",
    "SIGMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# independent noise\n",
    "cov = torch.tensor((params[\"sigma_0\"] ** 2) * np.identity(S)) + SIGMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.4295, 2.1354, 1.4860])"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch.distributions.multivariate_normal import MultivariateNormal\n",
    "\n",
    "distrib = MultivariateNormal(loc=X_t_beta, covariance_matrix=cov.float())\n",
    "y1 = distrib.rsample()\n",
    "y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.5000, 0.0000, 0.0000],\n",
       "        [0.0000, 0.5000, 0.0000],\n",
       "        [0.0000, 0.0000, 0.5000]])"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transition matrix\n",
    "PHI = torch.tensor(params[\"phi\"] * np.identity(S)).float()\n",
    "PHI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_t_minus_1 = y1\n",
    "X_t_minus_1_beta = X_t_beta  # assume fixed for all t, in this example\n",
    "\n",
    "y = y1\n",
    "\n",
    "for t in range(1, T):\n",
    "    mu_t = X_t_beta + torch.matmul(PHI, y_t_minus_1 - X_t_minus_1_beta)\n",
    "    distrib = MultivariateNormal(loc=mu_t, covariance_matrix=cov.float())\n",
    "    y_t = distrib.rsample()\n",
    "\n",
    "    y = torch.concat([y, y_t])\n",
    "    y_t_minus_1 = y_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.1344, 2.2652, 1.2914, 1.7141, 2.4238, 2.2781, 1.3430, 2.1860, 2.1145,\n",
       "        1.7693, 2.5683, 2.1675, 1.2174, 2.0566, 1.8388, 1.2711, 2.3781, 1.5542,\n",
       "        1.3536, 2.4000, 1.9223, 1.7035, 2.3589, 2.0143, 1.2242, 2.3603, 1.4980,\n",
       "        1.0990, 2.5295, 1.5559])"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 238,
     "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": [
    "pd.DataFrame(y.reshape(T, S).numpy()).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# how to implement this in codebase\n",
    "\n",
    "# just Gaussian process based on stream distance (spatial)\n",
    "\n",
    "# we have generative model, need likelihood (pyro? -> have a way to test it. In pure python, write down likelihood. Compare same data)\n",
    "\n",
    "\n",
    "# pyro -> once the generator is written, pyro gives likelihood for free\n",
    "\n",
    "# likelihood is the factorised Gaussian, uncorrelated errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stochastic volatility, environmental variables exhibit this. high-variability/low-variability -> maybe a generalisation\n",
    "# first and second order differencing (michael stein, slides) -> references"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## River as graph (NetworkX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\n10907700\\AppData\\Local\\Temp\\ipykernel_11216\\1569939559.py:11: DeprecationWarning: `alltrue` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `all` instead.\n",
      "  nx.draw_networkx_edges(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "\n",
    "# encode as networkx object and plot\n",
    "G = nx.DiGraph()\n",
    "G.add_weighted_edges_from(\n",
    "    list(zip(shapefile.pid_from, shapefile.pid_to, shapefile.addfunccol * 10))\n",
    ")\n",
    "# adding edges in the form: G.add_weighted_edges_from([(1, 2, 0.5), (3, 1, 1.75)])\n",
    "pos = nx.spring_layout(G)\n",
    "widths = nx.get_edge_attributes(G, \"weight\")\n",
    "nx.draw_networkx_edges(\n",
    "    G,\n",
    "    pos,\n",
    "    edgelist=widths.keys(),\n",
    "    width=list(widths.values()),\n",
    "    edge_color=\"darkblue\",\n",
    "    alpha=1,\n",
    ")\n",
    "plt.show()\n",
    "# nodelist = G.nodes()\n",
    "# nx.draw_networkx_nodes(G,pos,\n",
    "#                        nodelist=nodelist,\n",
    "#                        node_size=1500,\n",
    "#                        node_color='black',\n",
    "#                        alpha=0.7)\n",
    "# nx.draw_networkx_labels(G, pos=pos,\n",
    "#                         labels=dict(zip(nodelist,nodelist)),\n",
    "#                         font_color='white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dad_vtorch",
   "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": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}