Merge pull request #166 from LSSTDESC/issues/165

Updated dm_butler_lensing_cuts notebook
LSSTDESC · Mar 1, 2024 · 33a3a9c · 33a3a9c
2 parents 1653685 + 8391bb1
commit 33a3a9c
Showing 1 changed file with 66 additions and 13 deletions.
diff --git a/tutorials/dm_butler_lensing_cuts.ipynb b/tutorials/dm_butler_lensing_cuts.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Accessing the Coadd data using the Data Butler\n",
     "<br>Owner: **Jim Chiang** ([@jchiang87](https://github.com/LSSTDESC/DC2-analysis/issues/new?body=@jchiang87))\n",
-    "<br>Last Verified to Run: **2018-10-26** (by @yymao)\n",
+    "<br>Last Verified to Run: **2024-03-01** (by @fjaviersanchez)\n",
     "\n",
     "This notebook shows how to read in the coadd catalog data for an entire tract using the LSST Data Management (DM) data butler, and construct a Pandas dataframe with the columns needed to apply the weak lensing cuts used in recent analysis of data from Hyper Suprime Cam [Mandelbaum et al (2018)](https://arxiv.org/abs/1705.06745) using cuts given by [Francois in Slack](https://lsstc.slack.com/archives/C77DDKZHR/p1525197444000687).\n",
     "\n",
@@ -24,7 +24,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -49,7 +51,46 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "REPOS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tract = skymap[4851]\n",
+    "filter = 'i'\n",
+    "for patch in tract:\n",
+    "        patchId = '%d,%d' % patch.getIndex()\n",
+    "        dataId = dict(filter=filter_, tract=tract.getId(), patch=patchId)\n",
+    "\n",
+    "        # Patches for which there is no data will raise a `NoResults` exception when requested\n",
+    "        # from the butler.  We catch those exceptions, note the missing data, and \n",
+    "        # continue looping over the patches.\n",
+    "        try:\n",
+    "            forced = butler.get('deepCoadd_forced_src', dataId)\n",
+    "            calib = butler.get('deepCoadd_calexp_photoCalib', dataId)\n",
+    "            merged = butler.get('deepCoadd_ref', dataId)\n",
+    "        except dp.NoResults as eobj:\n",
+    "            print(eobj)\n",
+    "            continue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "def read_tract(butler, tract, filter_='i', num_patches=None):\n",
@@ -108,8 +149,10 @@
     "        data[filter_ + '_modelfit_CModel_instFluxErr'] = forced['modelfit_CModel_instFluxErr']\n",
     "\n",
     "        # The calib object applies the zero point to get calibrated magnitudes.\n",
-    "        data[filter_ + '_mag_CModel'] = calib.getMagnitude(forced['modelfit_CModel_instFlux'])\n",
-    "        data[filter_ + '_mag_err_CModel'] = calib.getMagnitude(forced['modelfit_CModel_instFluxErr'])\n",
+    "        \n",
+    "        _aux = calib.instFluxToMagnitude(forced, 'modelfit_CModel')\n",
+    "        data[filter_ + '_mag_CModel'] = _aux[:, 0]\n",
+    "        data[filter_ + '_mag_err_CModel'] = _aux[:, 1]\n",
     "        data[filter_ + '_modelfit_CModel_SNR'] \\\n",
     "            = forced['modelfit_CModel_instFlux']/forced['modelfit_CModel_instFluxErr']\n",
     "        data['ext_shapeHSM_HsmShapeRegauss_abs_e'] \\\n",
@@ -137,10 +180,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "butler = dp.Butler(REPOS['1.2i'])"
+    "butler = dp.Butler(REPOS['2.2i_dr6'])"
    ]
   },
   {
@@ -153,7 +198,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "skymap = butler.get('deepCoadd_skyMap')\n",
@@ -176,7 +223,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "base_mask = ~(np.isnan(data['i_modelfit_CModel_instFlux'])\n",
@@ -196,7 +245,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "mask = data['detect_isPrimary']\n",
@@ -216,7 +267,7 @@
     "mask &= data['ext_shapeHSM_HsmShapeRegauss_abs_e'] < 2\n",
     "mask &= data['ext_shapeHSM_HsmShapeRegauss_sigma'] <= 0.4\n",
     "mask &= data['i_mag_CModel'] < 24.5 # !!! Doesnt have exinction correction\n",
-    "mask &= data['base_Blendedness_abs_instFlux'] < 10**(-0.375)"
+    "mask &= data['base_Blendedness_abs'] < 10**(-0.375)"
    ]
   },
   {
@@ -229,7 +280,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "fig = plt.figure(figsize=(10, 10))\n",
@@ -282,5 +335,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }