{ "cells": [ { "cell_type": "markdown", "id": "9dd05350", "metadata": {}, "source": [ "# Regional Flux Inversion\n", "\n", "## Bayesian Inversion for Regional CH₄ Emissions\n", "\n", "This notebook demonstrates solving a regional atmospheric inverse problem using Bayesian estimation.\n", "\n", "### Problem Setup\n", "\n", "**State (x):** Regional CH₄ flux anomalies on a spatial grid [µmol/m²/s] \n", "**Observations (z):** CH₄ concentration enhancements at observation sites [ppm] \n", "**Forward model:** z = H @ x (atmospheric transport from fluxes to concentrations)\n", "\n", "### Approach\n", "\n", "- **Synthetic Jacobian:** Distance-based Gaussian sensitivity (flux → concentration)\n", "- **Synthetic Truth:** Gaussian emission hotspots within domain\n", "- **Synthetic Observations:** Concentrations from truth + noise\n", "- **Bayesian Estimation:** Recover fluxes from noisy concentration measurements" ] }, { "cell_type": "markdown", "id": "1dfdf4c0", "metadata": {}, "source": [ "## 1. Imports" ] }, { "cell_type": "code", "execution_count": 1, "id": "0fc7ad00", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from fips.problems.flux import FluxProblem\n", "\n", "# Configure plotting\n", "plt.style.use(\"seaborn-v0_8-darkgrid\")\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "id": "931b101b", "metadata": {}, "source": [ "## 2. Configuration" ] }, { "cell_type": "code", "execution_count": 2, "id": "10ced048", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Configuration:\n", " Domain: [-112.0, -111.5] × [40.5, 41.0] (spacing 0.05°)\n", " Time: 2024-01-01 00:00:00 to 2025-01-01 00:00:00\n", " Observation sites: 3\n", " Hotspots: 2 @ 1.0 µmol/m²/s\n", " Prior: 0.05 ± 0.025 µmol/m²/s\n" ] } ], "source": [ "# ==============================================================================\n", "# RANDOM SEED\n", "# ==============================================================================\n", "\n", "RANDOM_SEED = 42\n", "\n", "# ==============================================================================\n", "# DOMAIN CONFIGURATION\n", "# ==============================================================================\n", "\n", "# Spatial domain (lat/lon bounds)\n", "XMIN, XMAX = -112.0, -111.5 # longitude [deg]\n", "YMIN, YMAX = 40.5, 41.0 # latitude [deg]\n", "DX, DY = 0.05, 0.05 # grid spacing [deg]\n", "\n", "# Time domain\n", "T_START = pd.Timestamp(\"2024-01-01\")\n", "T_STOP = pd.Timestamp(\"2025-01-01\")\n", "STATE_FREQ = \"MS\" # state frequency\n", "OBS_FREQ = \"D\" # observation frequency\n", "\n", "# ==============================================================================\n", "# OBSERVATION SITES\n", "# ==============================================================================\n", "\n", "N_SITES = 3 # number of observation locations\n", "\n", "# ==============================================================================\n", "# EMISSION HOTSPOTS (Truth field)\n", "# ==============================================================================\n", "\n", "N_HOTSPOTS = 2 # number of Gaussian emission centers\n", "HOTSPOT_AMPLITUDE = 1.0 # peak flux [µmol/m²/s]\n", "HOTSPOT_WIDTH = 0.1 # Gaussian width [deg] (std dev)\n", "\n", "# ==============================================================================\n", "# PRIOR CONFIGURATION\n", "# ==============================================================================\n", "\n", "PRIOR_MEAN = 0.05 # prior flux mean [µmol/m²/s]\n", "PRIOR_STD_FRACTION = 0.5 # prior uncertainty as fraction of prior mean\n", "\n", "# ==============================================================================\n", "# JACOBIAN (SENSITIVITY)\n", "# ==============================================================================\n", "\n", "INFLUENCE_RADIUS = 0.3 # Gaussian influence radius [deg]\n", "\n", "# ==============================================================================\n", "# OBSERVATION ERROR\n", "# ==============================================================================\n", "\n", "OBS_NOISE_STD = 0.001 # observation noise [ppm]\n", "\n", "print(\"Configuration:\")\n", "print(f\" Domain: [{XMIN}, {XMAX}] × [{YMIN}, {YMAX}] (spacing {DX}°)\")\n", "print(f\" Time: {T_START} to {T_STOP}\")\n", "print(f\" Observation sites: {N_SITES}\")\n", "print(f\" Hotspots: {N_HOTSPOTS} @ {HOTSPOT_AMPLITUDE} µmol/m²/s\")\n", "print(f\" Prior: {PRIOR_MEAN} ± {PRIOR_MEAN * PRIOR_STD_FRACTION} µmol/m²/s\")" ] }, { "cell_type": "markdown", "id": "a55198bb", "metadata": {}, "source": [ "## 3. Helper Functions" ] }, { "cell_type": "code", "execution_count": 3, "id": "2fbf964c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Helper functions loaded.\n" ] } ], "source": [ "def build_grid(xmin, xmax, ymin, ymax, dx, dy):\n", " \"\"\"\n", " Build spatial grid.\n", "\n", " Returns\n", " -------\n", " lons : array\n", " Longitude cell centers\n", " lats : array\n", " Latitude cell centers\n", " \"\"\"\n", " lons = np.arange(xmin + dx / 2, xmax, dx)\n", " lats = np.arange(ymin + dy / 2, ymax, dy)\n", " return lons, lats\n", "\n", "\n", "def build_obs_sites(n_sites, xmin, xmax, ymin, ymax, seed=42):\n", " \"\"\"\n", " Generate random observation site locations within domain.\n", "\n", " Returns\n", " -------\n", " obs_lons : array\n", " Observation longitude coordinates\n", " obs_lats : array\n", " Observation latitude coordinates\n", " \"\"\"\n", " np.random.seed(seed)\n", " obs_lons = np.random.uniform(xmin, xmax, n_sites)\n", " obs_lats = np.random.uniform(ymin, ymax, n_sites)\n", " return obs_lons, obs_lats\n", "\n", "\n", "def gaussian_2d(x, y, x0, y0, sigma):\n", " \"\"\"\n", " Evaluate 2D Gaussian.\n", "\n", " Parameters\n", " ----------\n", " x, y : array\n", " Coordinates (can be meshgrid)\n", " x0, y0 : float\n", " Gaussian center\n", " sigma : float\n", " Gaussian width\n", "\n", " Returns\n", " -------\n", " z : array\n", " Gaussian values\n", " \"\"\"\n", " return np.exp(-0.5 * ((x - x0) ** 2 + (y - y0) ** 2) / sigma**2)\n", "\n", "\n", "def build_truth_field(lons, lats, hotspots, amplitude, width):\n", " \"\"\"\n", " Build synthetic truth field from Gaussian hotspots.\n", "\n", " Parameters\n", " ----------\n", " lons, lats : array\n", " Grid coordinates\n", " hotspots : list of (lon, lat)\n", " Hotspot center locations\n", " amplitude : float\n", " Peak flux amplitude\n", " width : float\n", " Gaussian width\n", "\n", " Returns\n", " -------\n", " flux : 2D array\n", " Flux field [n_lon × n_lat]\n", " \"\"\"\n", " X, Y = np.meshgrid(lons, lats, indexing=\"ij\")\n", " flux = np.zeros_like(X)\n", "\n", " for lon0, lat0 in hotspots:\n", " flux += amplitude * gaussian_2d(X, Y, lon0, lat0, width)\n", "\n", " return flux\n", "\n", "\n", "def build_jacobian(obs_lons, obs_lats, state_lons, state_lats, influence_radius):\n", " \"\"\"\n", " Build synthetic Jacobian using distance-based Gaussian sensitivity.\n", "\n", " H[i,j] = sensitivity of observation i to flux j\n", " = exp(-0.5 * (distance_ij / influence_radius)^2)\n", "\n", " Parameters\n", " ----------\n", " obs_lons, obs_lats : array\n", " Observation site coordinates\n", " state_lons, state_lats : array\n", " State grid coordinates\n", " influence_radius : float\n", " Gaussian influence radius\n", "\n", " Returns\n", " -------\n", " H : 2D array\n", " Jacobian [n_obs × n_state]\n", " \"\"\"\n", " n_obs = len(obs_lons)\n", " n_lon = len(state_lons)\n", " n_lat = len(state_lats)\n", " n_state = n_lon * n_lat\n", "\n", " H = np.zeros((n_obs, n_state))\n", "\n", " idx = 0\n", " for i_lon in range(n_lon):\n", " for i_lat in range(n_lat):\n", " state_lon = state_lons[i_lon]\n", " state_lat = state_lats[i_lat]\n", "\n", " for i_obs in range(n_obs):\n", " # Great-circle distance (simplified for small distances)\n", " dlon = obs_lons[i_obs] - state_lon\n", " dlat = obs_lats[i_obs] - state_lat\n", " distance = np.sqrt(dlon**2 + dlat**2)\n", "\n", " # Gaussian sensitivity\n", " H[i_obs, idx] = np.exp(-0.5 * (distance / influence_radius) ** 2)\n", "\n", " idx += 1\n", "\n", " return H\n", "\n", "\n", "print(\"Helper functions loaded.\")" ] }, { "cell_type": "markdown", "id": "5c5cae24", "metadata": {}, "source": [ "## 4. Build Domain and Observation Network" ] }, { "cell_type": "code", "execution_count": 4, "id": "fd9b06b4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Spatial grid: 10 × 10 = 100 cells\n", "Observation sites: 3\n", " Site 0: (-111.813, 40.799)\n", " Site 1: (-111.525, 40.578)\n", " Site 2: (-111.634, 40.578)\n", "Hotspots: 2\n", " Hotspot 0: (-111.942, 40.567)\n", " Hotspot 1: (-111.695, 40.620)\n" ] } ], "source": [ "# Build grid\n", "lons, lats = build_grid(XMIN, XMAX, YMIN, YMAX, DX, DY)\n", "X_grid, Y_grid = np.meshgrid(lons, lats, indexing=\"ij\")\n", "\n", "n_lon = len(lons)\n", "n_lat = len(lats)\n", "n_state_spatial = n_lon * n_lat\n", "\n", "print(f\"Spatial grid: {n_lon} × {n_lat} = {n_state_spatial} cells\")\n", "\n", "# Generate observation sites\n", "obs_lons, obs_lats = build_obs_sites(N_SITES, XMIN, XMAX, YMIN, YMAX, seed=RANDOM_SEED)\n", "location_mapper = {}\n", "print(f\"Observation sites: {N_SITES}\")\n", "for i, (lon, lat) in enumerate(zip(obs_lons, obs_lats, strict=False)):\n", " print(f\" Site {i}: ({lon:.3f}, {lat:.3f})\")\n", " location_mapper[i] = (lat, lon)\n", "\n", "# Generate hotspot locations\n", "np.random.seed(RANDOM_SEED + 1)\n", "hotspot_lons = np.random.uniform(XMIN, XMAX, N_HOTSPOTS)\n", "hotspot_lats = np.random.uniform(YMIN, YMAX, N_HOTSPOTS)\n", "hotspots = list(zip(hotspot_lons, hotspot_lats, strict=False))\n", "\n", "print(f\"Hotspots: {N_HOTSPOTS}\")\n", "for i, (lon, lat) in enumerate(hotspots):\n", " print(f\" Hotspot {i}: ({lon:.3f}, {lat:.3f})\")" ] }, { "cell_type": "markdown", "id": "65df3cd4", "metadata": {}, "source": [ "## 5. Build Time Grids" ] }, { "cell_type": "code", "execution_count": 5, "id": "fb05daa3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "State time bins: 12\n", " 2024-01-01 00:00:00\n", " 2024-02-01 00:00:00\n", " 2024-03-01 00:00:00\n", " 2024-04-01 00:00:00\n", " 2024-05-01 00:00:00\n", " 2024-06-01 00:00:00\n", " 2024-07-01 00:00:00\n", " 2024-08-01 00:00:00\n", " 2024-09-01 00:00:00\n", " 2024-10-01 00:00:00\n", " 2024-11-01 00:00:00\n", " 2024-12-01 00:00:00\n", "\n", "Observation times: 366\n", " 2024-01-01 00:00:00 to 2024-12-31 00:00:00\n" ] } ], "source": [ "# State time bins (fluxes)\n", "state_times = pd.date_range(\n", " start=T_START, end=T_STOP, freq=STATE_FREQ, inclusive=\"left\"\n", ")\n", "n_time_state = len(state_times)\n", "\n", "# Observation time bins\n", "obs_times = pd.date_range(start=T_START, end=T_STOP, freq=OBS_FREQ, inclusive=\"left\")\n", "n_time_obs = len(obs_times)\n", "\n", "print(f\"State time bins: {n_time_state}\")\n", "for t in state_times:\n", " print(f\" {t}\")\n", "\n", "print(f\"\\nObservation times: {n_time_obs}\")\n", "print(f\" {obs_times[0]} to {obs_times[-1]}\")" ] }, { "cell_type": "markdown", "id": "479085d0", "metadata": {}, "source": [ "## 6. Build Jacobian" ] }, { "cell_type": "code", "execution_count": 6, "id": "a1260deb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Building Jacobian...\n", "Jacobian shape: (3, 100)\n", " Range: [0.1350, 0.9999]\n", " Mean sensitivity: 0.6698\n" ] } ], "source": [ "print(\"Building Jacobian...\")\n", "H = build_jacobian(obs_lons, obs_lats, lons, lats, INFLUENCE_RADIUS)\n", "\n", "print(f\"Jacobian shape: {H.shape}\")\n", "print(f\" Range: [{H.min():.4f}, {H.max():.4f}]\")\n", "print(f\" Mean sensitivity: {H.mean():.4f}\")" ] }, { "cell_type": "markdown", "id": "444f528a", "metadata": {}, "source": [ "## 7. Generate Synthetic Truth and Observations" ] }, { "cell_type": "code", "execution_count": 7, "id": "edea173f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Building truth field with seasonal cycle...\n", "Truth field: mean=0.3525, max=1.5691\n", " Seasonal range: 0.0001 to 1.5691\n", "Prior field: 0.0500 ± 0.0250\n" ] } ], "source": [ "# Build truth field with seasonal cycle\n", "print(\"Building truth field with seasonal cycle...\")\n", "truth_2d = build_truth_field(lons, lats, hotspots, HOTSPOT_AMPLITUDE, HOTSPOT_WIDTH)\n", "\n", "# Create time-varying truth with seasonal cycle\n", "# Amplitude varies from 0.5x to 1.5x the base, peaking in summer (day ~180)\n", "truth_3d = np.zeros((n_lon, n_lat, n_time_state))\n", "\n", "for t_idx, t in enumerate(state_times):\n", " # Seasonal cycle: sinusoid with peak in summer\n", " # day_of_year: 0-365, peak amplitude at day ~180 (June-July)\n", " day_of_year = t.dayofyear\n", " seasonal_factor = 1.0 + 0.5 * np.sin(2 * np.pi * (day_of_year - 152) / 365.25)\n", " truth_3d[:, :, t_idx] = seasonal_factor * truth_2d\n", "\n", "# Reshape to flat vector in the correct order: iterate over lon, lat, then time\n", "truth_data = truth_3d.reshape(-1)\n", "\n", "# Create state index to match this ordering\n", "state_index = pd.MultiIndex.from_product(\n", " [lons, lats, state_times], names=[\"lon\", \"lat\", \"time\"]\n", ")\n", "\n", "truth_series = pd.Series(truth_data, index=state_index, name=\"truth\")\n", "\n", "print(f\"Truth field: mean={truth_series.mean():.4f}, max={truth_series.max():.4f}\")\n", "print(f\" Seasonal range: {truth_series.min():.4f} to {truth_series.max():.4f}\")\n", "\n", "# Prior (uniform, independent of season)\n", "prior_series = pd.Series(PRIOR_MEAN, index=state_index, name=\"flux\")\n", "print(f\"Prior field: {prior_series.mean():.4f} ± {PRIOR_MEAN * PRIOR_STD_FRACTION:.4f}\")" ] }, { "cell_type": "markdown", "id": "aaf84013", "metadata": {}, "source": [ "## 8. Generate Synthetic Observations" ] }, { "cell_type": "code", "execution_count": 8, "id": "47808f72", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating synthetic observations...\n", "Observations: 1098\n", " Range: [11.480887, 42.115867]\n", " Mean: 26.427648\n" ] } ], "source": [ "print(\"Generating synthetic observations...\")\n", "\n", "# For each observation time, compute concentrations from time-varying fluxes\n", "syn_obs_data = []\n", "syn_obs_index = []\n", "\n", "for t_obs in obs_times:\n", " # Find nearest state time\n", " idx = np.searchsorted(state_times, t_obs, side=\"left\")\n", " if idx >= n_time_state:\n", " idx = n_time_state - 1\n", "\n", " # Extract truth for this time step from the 3D array\n", " state_flat = truth_3d[:, :, idx].ravel()\n", "\n", " # Compute concentrations\n", " conc = H @ state_flat\n", "\n", " # Add noise\n", " noise = np.random.normal(0, OBS_NOISE_STD, N_SITES)\n", " conc_noisy = conc + noise\n", "\n", " syn_obs_data.extend(conc_noisy)\n", " for i_site in range(N_SITES):\n", " syn_obs_index.append((i_site, t_obs))\n", "\n", "obs_index = pd.MultiIndex.from_tuples(syn_obs_index, names=[\"site_idx\", \"time\"])\n", "syn_obs = pd.Series(syn_obs_data, index=obs_index, name=\"concentration\")\n", "\n", "print(f\"Observations: {len(syn_obs)}\")\n", "print(f\" Range: [{syn_obs.min():.6f}, {syn_obs.max():.6f}]\")\n", "print(f\" Mean: {syn_obs.mean():.6f}\")" ] }, { "cell_type": "markdown", "id": "64c2bd77", "metadata": {}, "source": [ "## 9. Setup Inverse Problem" ] }, { "cell_type": "code", "execution_count": 9, "id": "a474e521", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Setting up flux inversion problem...\n", "Flux inversion problem configured.\n", " State dimension: 1200\n", " Observation dimension: 1098\n", " Jacobian shape: (1098, 1200)\n" ] } ], "source": [ "print(\"Setting up flux inversion problem...\")\n", "\n", "# Prior covariance (diagonal)\n", "prior_std = PRIOR_MEAN * PRIOR_STD_FRACTION\n", "S_0 = pd.DataFrame(\n", " np.diag((prior_std**2) * np.ones(len(prior_series))),\n", " index=state_index,\n", " columns=state_index,\n", ")\n", "\n", "# Observation covariance (diagonal)\n", "S_z = pd.DataFrame(\n", " np.diag((OBS_NOISE_STD**2) * np.ones(len(syn_obs))),\n", " index=obs_index,\n", " columns=obs_index,\n", ")\n", "\n", "# Forward operator: map state (lon, lat, time_state) to obs (site, time_obs)\n", "# The state_index is organized as: (lon[0], lat[0], t[0]), (lon[0], lat[0], t[1]), ...,\n", "# (lon[0], lat[1], t[0]), (lon[0], lat[1], t[1]), ...\n", "# So for each observation at time t_obs, we need to find all spatial cells at the corresponding state time\n", "H_full = np.zeros((len(syn_obs), len(prior_series)))\n", "\n", "obs_row = 0\n", "for t_obs in obs_times:\n", " # Find corresponding state time index\n", " state_time_idx = np.searchsorted(state_times, t_obs, side=\"left\")\n", " if state_time_idx >= n_time_state:\n", " state_time_idx = n_time_state - 1\n", "\n", " # For each observation site at this time\n", " for site_idx in range(N_SITES):\n", " # Fill in Jacobian row for all spatial locations\n", " for lon_idx in range(n_lon):\n", " for lat_idx in range(n_lat):\n", " spatial_idx = lon_idx * n_lat + lat_idx\n", " col_pos = spatial_idx * n_time_state + state_time_idx\n", " H_full[obs_row, col_pos] = H[site_idx, spatial_idx]\n", " obs_row += 1\n", "\n", "H_mat = pd.DataFrame(H_full, index=obs_index, columns=state_index)\n", "\n", "# Create flux inversion problem\n", "# The prepare functions will handle conversion of pandas objects to Vector/Matrix types\n", "problem = FluxProblem(\n", " obs=syn_obs,\n", " prior=prior_series,\n", " forward_operator=H_mat,\n", " prior_error=S_0,\n", " modeldata_mismatch=S_z,\n", ")\n", "\n", "print(\"Flux inversion problem configured.\")\n", "print(f\" State dimension: {len(prior_series)}\")\n", "print(f\" Observation dimension: {len(syn_obs)}\")\n", "print(f\" Jacobian shape: {H_full.shape}\")" ] }, { "cell_type": "markdown", "id": "54c75e03", "metadata": {}, "source": [ "## 10. Solve" ] }, { "cell_type": "code", "execution_count": 10, "id": "c27885e6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solving...\n", "✓ Solution complete\n" ] } ], "source": [ "print(\"Solving...\")\n", "problem.solve(estimator=\"bayesian\")\n", "print(\"✓ Solution complete\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "a58c494e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Vector(name='None', shape=(1098,))\n", "block site_idx time \n", "concentration 0 2024-01-01 19.783718\n", " 1 2024-01-01 17.022029\n", " 2 2024-01-01 20.813514\n", " 0 2024-01-02 14.939326\n", " 1 2024-01-02 12.854288\n", " ... \n", " 2024-12-30 22.693028\n", " 2 2024-12-30 27.748752\n", " 0 2024-12-31 26.374392\n", " 1 2024-12-31 22.694512\n", " 2 2024-12-31 27.747134\n", "Name: None, Length: 1098, dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "problem.obs" ] }, { "cell_type": "markdown", "id": "5a4eb9d3", "metadata": {}, "source": [ "## 11. Diagnostics" ] }, { "cell_type": "code", "execution_count": 12, "id": "a61816bd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "POSTERIOR STATE (FLUX)\n", " Mean: 0.3492 µmol/m²/s\n", " Std: 0.3724 µmol/m²/s\n", " Range: [-0.2208, 1.4783]\n", "\n", "OBSERVATION FIT\n", " RMSE: 1.017315e-03 ppm\n", " R²: 1.0000\n", " Max residual: 5.317285e-03 ppm\n", "\n", "UNCERTAINTY REDUCTION\n", " Mean: 1.5%\n", " Range: [0.7%, 3.2%]\n", "\n", "INFORMATION CONTENT\n", " DOFS: 36.0\n", " Reduced χ²: 400.136\n" ] } ], "source": [ "# Extract results\n", "x_post = problem.posterior.data.values\n", "z_obs_vals = problem.obs.data.values\n", "z_post = problem.posterior_obs.data.values\n", "z_prior = problem.prior_obs.data.values\n", "\n", "# Posterior state diagnostics\n", "print(\"POSTERIOR STATE (FLUX)\")\n", "print(f\" Mean: {x_post.mean():.4f} µmol/m²/s\")\n", "print(f\" Std: {x_post.std():.4f} µmol/m²/s\")\n", "print(f\" Range: [{x_post.min():.4f}, {x_post.max():.4f}]\")\n", "\n", "# Observation fit\n", "print(\"\\nOBSERVATION FIT\")\n", "print(f\" RMSE: {problem.estimator.RMSE:.6e} ppm\")\n", "print(f\" R²: {problem.estimator.R2:.4f}\")\n", "residuals = z_obs_vals - z_post\n", "print(f\" Max residual: {np.abs(residuals).max():.6e} ppm\")\n", "\n", "# Uncertainty reduction\n", "prior_std_full = np.sqrt(np.diag(problem.prior_error.values))\n", "post_std_full = np.sqrt(np.diag(problem.posterior_error.values))\n", "reduction = (1 - post_std_full / prior_std_full) * 100\n", "\n", "print(\"\\nUNCERTAINTY REDUCTION\")\n", "print(f\" Mean: {reduction.mean():.1f}%\")\n", "print(f\" Range: [{reduction.min():.1f}%, {reduction.max():.1f}%]\")\n", "\n", "# Information content\n", "print(\"\\nINFORMATION CONTENT\")\n", "print(f\" DOFS: {problem.estimator.DOFS:.1f}\")\n", "print(f\" Reduced χ²: {problem.estimator.reduced_chi2:.3f}\")" ] }, { "cell_type": "markdown", "id": "9c9ddb5f", "metadata": {}, "source": [ "## 12. Visualization" ] }, { "cell_type": "code", "execution_count": 13, "id": "d5821e77", "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [ { "data": { "text/plain": [ "(
,\n", " array([,\n", " ,\n", " ,\n", " ], dtype=object))" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "problem.plot.fluxes(truth=truth_series, sites=location_mapper)" ] }, { "cell_type": "code", "execution_count": 14, "id": "404ba158", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "array([,\n", " ,\n", " ],\n", " dtype=object)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "problem.plot.concentrations(location_dim=\"site_idx\")" ] }, { "cell_type": "code", "execution_count": 15, "id": "3233346a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "problem.posterior[\"flux\"].to_xarray().plot(x=\"lon\", y=\"lat\", col=\"time\", col_wrap=4)" ] } ], "metadata": { "kernelspec": { "display_name": "fips", "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": "3.10.18" } }, "nbformat": 4, "nbformat_minor": 5 }