{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 02 - Configuration and Coordinates\n",
    "\n",
    "## Purpose\n",
    "\n",
    "This tutorial explains two key components within SMPy:\n",
    "\n",
    "1. **Configuration** controls reproducibility.\n",
    "2. **Coordinate system choice** controls physical interpretation.\n",
    "\n",
    "The notebook is intentionally short and conceptual."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Configuration Anatomy\n",
    "\n",
    "Think of the config as a scientific contract:\n",
    "\n",
    "- `general`: data source, coordinate system, output behavior\n",
    "- `methods`: algorithm-specific parameters\n",
    "- `plotting`: map rendering choices\n",
    "- `snr`: null-realization controls\n",
    "\n",
    "Every single tunable parameter in the model is defined in the configuration yaml file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "83de2952",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python package version: 0.5.0\n",
      "Random seed: 42\n",
      "Data file: /Users/vassig/research/SMPy/examples/data/forecast_lum_annular.fits\n",
      "Artifacts: /Users/vassig/research/SMPy/examples/outputs/tutorials\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yaml\n",
    "\n",
    "import smpy\n",
    "from IPython.display import Image, display\n",
    "from smpy.config import Config\n",
    "from smpy.run import run\n",
    "\n",
    "SEED = 42\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "\n",
    "\n",
    "def find_repo_root(start: Path) -> Path:\n",
    "    for candidate in [start, *start.parents]:\n",
    "        if (candidate / \"setup.py\").exists() and (candidate / \"smpy\").is_dir():\n",
    "            return candidate\n",
    "    raise RuntimeError(\"Could not locate SMPy repository root.\")\n",
    "\n",
    "\n",
    "repo_root = find_repo_root(Path.cwd().resolve())\n",
    "data_file = repo_root / \"examples\" / \"data\" / \"forecast_lum_annular.fits\"\n",
    "artifacts_dir = repo_root / \"examples\" / \"outputs\" / \"tutorials\"\n",
    "artifacts_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "print(f\"Python package version: {smpy.__version__}\")\n",
    "print(f\"Random seed: {SEED}\")\n",
    "print(f\"Data file: {data_file}\")\n",
    "print(f\"Artifacts: {artifacts_dir}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ade0c64a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validated config sections: ['general', 'methods', 'plotting', 'snr']\n",
      "Coordinate system: radec\n",
      "Method: kaiser_squires\n",
      "\n",
      "Configuration:\n",
      "\n",
      "General:\n",
      "  input_path: /Users/vassig/research/SMPy/examples/data/forecast_lum_annular.fits\n",
      "  input_hdu: 1\n",
      "  output_directory: /Users/vassig/research/SMPy/examples/outputs/tutorials\n",
      "  output_base_name: tutorial02_minimal\n",
      "  coordinate_system: radec\n",
      "  radec: {'resolution': 0.4, 'coord1': 'ra', 'coord2': 'dec'}\n",
      "  pixel: {'downsample_factor': 1, 'coord1': 'X_IMAGE', 'coord2': 'Y_IMAGE', 'pixel_axis_reference': 'catalog'}\n",
      "  g1_col: g1_Rinv\n",
      "  g2_col: g2_Rinv\n",
      "  weight_col: weight\n",
      "  method: kaiser_squires\n",
      "  create_snr: False\n",
      "  create_counts_map: False\n",
      "  overlay_counts_map: False\n",
      "  mode: ['E']\n",
      "  print_timing: False\n",
      "  save_fits: False\n",
      "  save_plots: False\n",
      "  _coord_system_set_by_user: True\n",
      "  _pixel_scale_set_by_user: True\n",
      "\n",
      "Methods:\n",
      "  kaiser_squires: {'smoothing': {'type': 'gaussian', 'sigma': 2.0}}\n",
      "  aperture_mass: {'filter': {'type': 'schirmer', 'scale': 60, 'truncation': 1.0, 'l': 3}}\n",
      "  ks_plus: {'inpainting_iterations': 100, 'reduced_shear_iterations': 3, 'nscales': None, 'extension_size': 'double', 'use_wavelet_constraints': True, 'constrain_B': False, 'threshold_schedule': 'exp', 'threshold_tau': None, 'smoothing': {'type': 'gaussian', 'sigma': 2.0}}\n",
      "\n",
      "Plotting:\n",
      "  figsize: [12, 8]\n",
      "  fontsize: 15\n",
      "  cmap: viridis\n",
      "  xlabel: auto\n",
      "  ylabel: auto\n",
      "  plot_title: Mass Map\n",
      "  gridlines: True\n",
      "  vmax: None\n",
      "  vmin: None\n",
      "  threshold: None\n",
      "  verbose: None\n",
      "  cluster_center: None\n",
      "  scaling: {'type': 'linear', 'gamma': 2, 'percentile': None, 'convergence': {'linthresh': 0.1, 'linscale': 1.0}, 'snr': {'linthresh': 5, 'linscale': 0.5}}\n",
      "\n",
      "Snr:\n",
      "  shuffle_type: spatial\n",
      "  num_shuffles: 100\n",
      "  seed: 0\n",
      "  smoothing: {'type': 'gaussian', 'sigma': 2.0}\n",
      "  plot_title: Signal-to-Noise Map\n"
     ]
    }
   ],
   "source": [
    "config = Config.from_defaults(\"kaiser_squires\")\n",
    "config.update_from_kwargs(\n",
    "    data=str(data_file),\n",
    "    coord_system=\"radec\",\n",
    "    pixel_scale=0.4,\n",
    "    g1_col=\"g1_Rinv\",\n",
    "    g2_col=\"g2_Rinv\",\n",
    "    weight_col=\"weight\",\n",
    "    mode=[\"E\"],\n",
    "    save_plots=False,\n",
    "    save_fits=False,\n",
    "    output_dir=str(artifacts_dir),\n",
    "    output_base_name=\"tutorial02_minimal\",\n",
    ")\n",
    "config.validate()\n",
    "\n",
    "cfg = config.to_dict()\n",
    "print(\"Validated config sections:\", list(cfg.keys()))\n",
    "print(\"Coordinate system:\", cfg[\"general\"][\"coordinate_system\"])\n",
    "print(\"Method:\", cfg[\"general\"][\"method\"])\n",
    "\n",
    "# Print the config in a readable format\n",
    "print(\"\\nConfiguration:\")\n",
    "for section, options in cfg.items():\n",
    "    print(f\"\\n{section.capitalize()}:\")\n",
    "    for key, value in options.items():\n",
    "        print(f\"  {key}: {value}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c69ef9f",
   "metadata": {},
   "source": [
    "## 2) Reproducibility Essentials\n",
    "\n",
    "Good baseline practice:\n",
    "\n",
    "- Save the exact config used for a run.\n",
    "- Keep output names stable and explicit.\n",
    "- Log package versions and seed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a31590f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Configuration saved to: /Users/vassig/research/SMPy/examples/outputs/tutorials/config_minimal.yaml\n",
      "Saved and reloaded: /Users/vassig/research/SMPy/examples/outputs/tutorials/config_minimal.yaml\n",
      "Reloaded coordinate system: radec\n"
     ]
    }
   ],
   "source": [
    "minimal_config_path = artifacts_dir / \"config_minimal.yaml\"\n",
    "config.save_config(minimal_config_path)\n",
    "\n",
    "reloaded = Config.from_file(minimal_config_path)\n",
    "reloaded.validate()\n",
    "\n",
    "print(f\"Saved and reloaded: {minimal_config_path}\")\n",
    "print(\"Reloaded coordinate system:\", reloaded.to_dict()[\"general\"][\"coordinate_system\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e78a94c4",
   "metadata": {},
   "source": [
    "## 3) Coordinate Systems: How to Choose\n",
    "\n",
    "Use this quick rule:\n",
    "\n",
    "| Data columns look like | Use | Main scale knob |\n",
    "|---|---|---|\n",
    "| `ra`, `dec` in sky coordinates | `radec` | `pixel_scale` (arcmin/pixel) |\n",
    "| detector/image coordinates (`X_*`, `Y_*`) | `pixel` | `downsample_factor` |\n",
    "\n",
    "Important distinction:\n",
    "\n",
    "- `pixel_scale` sets angular map resolution in sky coordinates.\n",
    "- `downsample_factor` coarsens a native pixel-coordinate grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72a23844",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Configuration saved to: /Users/vassig/research/SMPy/examples/outputs/tutorials/config_radec.yaml\n",
      "Convergence map saved as PNG file: /Users/vassig/research/SMPy/examples/outputs/tutorials/kaiser_squires/tutorial02_radec_kaiser_squires_e_mode.png\n",
      "Configuration saved to: /Users/vassig/research/SMPy/examples/outputs/tutorials/config_pixel.yaml\n",
      "Convergence map saved as PNG file: /Users/vassig/research/SMPy/examples/outputs/tutorials/kaiser_squires/tutorial02_pixel_kaiser_squires_e_mode.png\n",
      "RA/Dec map shape: (38, 57)\n",
      "Pixel map shape: (38, 57)\n"
     ]
    }
   ],
   "source": [
    "def make_base_config(output_base_name: str) -> Config:\n",
    "    c = Config.from_defaults(\"kaiser_squires\")\n",
    "    c.update_from_kwargs(\n",
    "        data=str(data_file),\n",
    "        g1_col=\"g1_Rinv\",\n",
    "        g2_col=\"g2_Rinv\",\n",
    "        weight_col=\"weight\",\n",
    "        mode=[\"E\"],\n",
    "        save_plots=True,\n",
    "        save_fits=False,\n",
    "        output_dir=str(artifacts_dir),\n",
    "        output_base_name=output_base_name,\n",
    "    )\n",
    "    return c\n",
    "\n",
    "\n",
    "# RA/Dec configuration\n",
    "radec_config = make_base_config(\"tutorial02_radec\")\n",
    "radec_config.update_from_kwargs(coord_system=\"radec\", pixel_scale=0.4)\n",
    "radec_config_path = artifacts_dir / \"config_radec.yaml\"\n",
    "radec_config.save_config(radec_config_path)\n",
    "radec_result = run(radec_config)\n",
    "\n",
    "# Pixel configuration (explicitly map coordinate column names)\n",
    "pixel_config = make_base_config(\"tutorial02_pixel\")\n",
    "pixel_config.update_from_kwargs(coord_system=\"pixel\", downsample_factor=170)\n",
    "pixel_dict = pixel_config.to_dict()\n",
    "pixel_dict[\"general\"][\"pixel\"][\"coord1\"] = \"X_IMAGE_se\"\n",
    "pixel_dict[\"general\"][\"pixel\"][\"coord2\"] = \"Y_IMAGE_se\"\n",
    "pixel_config = Config(pixel_dict)\n",
    "pixel_config.validate()\n",
    "pixel_config_path = artifacts_dir / \"config_pixel.yaml\"\n",
    "pixel_config.save_config(pixel_config_path)\n",
    "pixel_result = run(pixel_config)\n",
    "\n",
    "print(\"RA/Dec map shape:\", radec_result[\"maps\"][\"E\"].shape)\n",
    "print(\"Pixel map shape:\", pixel_result[\"maps\"][\"E\"].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "45f87f78",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coordinate_system</th>\n",
       "      <th>map_shape</th>\n",
       "      <th>pixel_scale_arcmin</th>\n",
       "      <th>mean_E</th>\n",
       "      <th>std_E</th>\n",
       "      <th>min_E</th>\n",
       "      <th>max_E</th>\n",
       "      <th>downsample_factor</th>\n",
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       "      <td>0.042876</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>pixel</td>\n",
       "      <td>38x57</td>\n",
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       "      <td>-4.100547e-19</td>\n",
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       "  coordinate_system map_shape  pixel_scale_arcmin        mean_E     std_E  \\\n",
       "0             radec     38x57                 0.4  2.460328e-18  0.042876   \n",
       "1             pixel     38x57                 NaN -4.100547e-19  0.043971   \n",
       "\n",
       "      min_E     max_E  downsample_factor  \n",
       "0 -0.104254  0.165443                NaN  \n",
       "1 -0.108428  0.165440              170.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = []\n",
    "for label, res, cfg in [\n",
    "    (\"radec\", radec_result, radec_config.to_dict()),\n",
    "    (\"pixel\", pixel_result, pixel_config.to_dict()),\n",
    "]:\n",
    "    e = res[\"maps\"][\"E\"]\n",
    "    if label == \"radec\":\n",
    "        scale_value = cfg[\"general\"][\"radec\"][\"resolution\"]\n",
    "        scale_label = \"pixel_scale_arcmin\"\n",
    "    else:\n",
    "        scale_value = cfg[\"general\"][\"pixel\"][\"downsample_factor\"]\n",
    "        scale_label = \"downsample_factor\"\n",
    "\n",
    "    rows.append(\n",
    "        {\n",
    "            \"coordinate_system\": label,\n",
    "            \"map_shape\": f\"{e.shape[0]}x{e.shape[1]}\",\n",
    "            scale_label: scale_value,\n",
    "            \"mean_E\": float(np.nanmean(e)),\n",
    "            \"std_E\": float(np.nanstd(e)),\n",
    "            \"min_E\": float(np.nanmin(e)),\n",
    "            \"max_E\": float(np.nanmax(e)),\n",
    "        }\n",
    "    )\n",
    "\n",
    "summary = pd.DataFrame(rows)\n",
    "summary_path = artifacts_dir / \"geometry_comparison.csv\"\n",
    "summary.to_csv(summary_path, index=False)\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f883e73a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMPy plotter output files:\n",
      "- RA/Dec map: /Users/vassig/research/SMPy/examples/outputs/tutorials/kaiser_squires/tutorial02_radec_kaiser_squires_e_mode.png\n",
      "- Pixel map: /Users/vassig/research/SMPy/examples/outputs/tutorials/kaiser_squires/tutorial02_pixel_kaiser_squires_e_mode.png\n",
      "- Summary table: /Users/vassig/research/SMPy/examples/outputs/tutorials/geometry_comparison.csv\n"
     ]
    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "radec_png = (\n",
    "    artifacts_dir\n",
    "    / \"kaiser_squires\"\n",
    "    / f\"{radec_config.to_dict()['general']['output_base_name']}_kaiser_squires_e_mode.png\"\n",
    ")\n",
    "pixel_png = (\n",
    "    artifacts_dir\n",
    "    / \"kaiser_squires\"\n",
    "    / f\"{pixel_config.to_dict()['general']['output_base_name']}_kaiser_squires_e_mode.png\"\n",
    ")\n",
    "\n",
    "print(\"SMPy plotter output files:\")\n",
    "print(f\"- RA/Dec map: {radec_png}\")\n",
    "print(f\"- Pixel map: {pixel_png}\")\n",
    "print(f\"- Summary table: {summary_path}\")\n",
    "\n",
    "if radec_png.exists() and pixel_png.exists():\n",
    "    display(Image(filename=str(radec_png)))\n",
    "    display(Image(filename=str(pixel_png)))\n",
    "else:\n",
    "    print(\"One or more expected PNG files were not found. Re-run the map cell above.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f89fd1eb",
   "metadata": {},
   "source": [
    "## 4) Errors and Misconfigurations\n",
    "\n",
    "When configuration and coordinate parameters disagree, SMPy fails fast. This is intentional!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2d212cf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected validation error:\n",
      "Missing required parameter for coordinate_system='radec'. Provide 'pixel_scale' (API: pixel_scale=..., YAML: general.radec.resolution).\n"
     ]
    }
   ],
   "source": [
    "bad_config = Config.from_defaults(\"kaiser_squires\")\n",
    "bad_config.update_from_kwargs(\n",
    "    data=str(data_file),\n",
    "    coord_system=\"radec\",\n",
    "    g1_col=\"g1_Rinv\",\n",
    "    g2_col=\"g2_Rinv\",\n",
    "    weight_col=\"weight\",\n",
    ")\n",
    "\n",
    "try:\n",
    "    bad_config.validate()\n",
    "except ValueError as exc:\n",
    "    print(\"Expected validation error:\")\n",
    "    print(exc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Practical Checklist\n",
    "\n",
    "Before running:\n",
    "\n",
    "1. Confirm your coordinate columns match your chosen coordinate system (and data/file format).\n",
    "2. For `radec`, set `pixel_scale` in arcmin/pixel.\n",
    "3. For `pixel`, set `downsample_factor` and pixel coordinate column names.\n",
    "4. Save your config with the run outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b512889",
   "metadata": {},
   "source": []
  }
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