From 1848d58bdf9dbed67c2ae52b8141f0565371e589 Mon Sep 17 00:00:00 2001 From: rlopezcoto Date: Fri, 24 Apr 2020 17:37:39 +0200 Subject: [PATCH] remove magic-lst directory --- lstchain/magic-lst/Display_MAGIC_event.ipynb | 187 --- lstchain/magic-lst/M1_magic_data.h5 | Bin 134128 -> 0 bytes lstchain/magic-lst/MAGICCam.camgeom.fits | Bin 40320 -> 0 bytes .../magic-lst/MAGIC_Camera_Geometry.ipynb | 286 ----- lstchain/magic-lst/MAGIC_LST1_analysis.pdf | Bin 1008277 -> 0 bytes lstchain/magic-lst/MAGIC_data_to_hdf5.ipynb | 211 ---- lstchain/magic-lst/README.md | 16 - lstchain/magic-lst/add_pointingpos.cc | 138 --- lstchain/magic-lst/magic_camera_info.txt | 1039 ----------------- 9 files changed, 1877 deletions(-) delete mode 100644 lstchain/magic-lst/Display_MAGIC_event.ipynb delete mode 100644 lstchain/magic-lst/M1_magic_data.h5 delete mode 100644 lstchain/magic-lst/MAGICCam.camgeom.fits delete mode 100644 lstchain/magic-lst/MAGIC_Camera_Geometry.ipynb delete mode 100644 lstchain/magic-lst/MAGIC_LST1_analysis.pdf delete mode 100644 lstchain/magic-lst/MAGIC_data_to_hdf5.ipynb delete mode 100644 lstchain/magic-lst/README.md delete mode 100644 lstchain/magic-lst/add_pointingpos.cc delete mode 100644 lstchain/magic-lst/magic_camera_info.txt diff --git a/lstchain/magic-lst/Display_MAGIC_event.ipynb b/lstchain/magic-lst/Display_MAGIC_event.ipynb deleted file mode 100644 index 8a0ad5d00b..0000000000 --- a/lstchain/magic-lst/Display_MAGIC_event.ipynb +++ /dev/null @@ -1,187 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from subprocess import call\n", - "import os\n", - "os.system(\"export CTAPIPE_SVC_PATH=$CTAPIPE_SVC_PATH:$PWD\")\n", - "os.system(\"echo $CTAPIPE_SVC_PATH\")\n", - "# Change\n", - "os.environ[\"CTAPIPE_SVC_PATH\"]=\"DIR_OF_THIS_SCRIPT\"\n", - "call(\"ls\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/MAGIC (Table(11,)) 'Table of MAGIC images'\n", - "/MAGIC (Table(11,)) 'Table of MAGIC images'\n", - " description := {\n", - " \"arrival_time\": Float16Col(shape=(1039,), dflt=0.0, pos=0),\n", - " \"event_index\": Int16Col(shape=(), dflt=0, pos=1),\n", - " \"event_time\": Float32Col(shape=(), dflt=0.0, pos=2),\n", - " \"image_charge\": Float16Col(shape=(1039,), dflt=0.0, pos=3),\n", - " \"pointing\": Float16Col(shape=(2,), dflt=0.0, pos=4),\n", - " \"trigger_number\": Int16Col(shape=(), dflt=0, pos=5)}\n", - " byteorder := 'little'\n", - " chunkshape := (31,)\n", - "DEBUG\n", - "(1039,)\n", - "[ 5.484 7.344 7.28 7.844 5.22 5.312 7.906 16.75 14.56 15.16\n", - " 13.47 14.47 8.47 6.562 12.41 36.75 27.56 19.31 10.19 5.67\n", - " 12.78 18.94 38.38 36.12 22. 15.59 8.06 20.44 8.84 8.125\n", - " 6.953 23.06 27.94 33.5 29.94 15.75 13.72 8.31 7.734 6.5\n", - " 14.97 28.06 30.62 15.75 17.25 8.41 6.67 7.516 11.56 20.44\n", - " 10.91 11.75 11.875 8.03 5.266 6.438 11.53 6.17 10.03 11.625\n", - " 7.25 5.33 ]\n", - "MomentParameters(size=877.859375, cen_x=, cen_y=, length=, width=, r=, phi=, psi=, miss=, skewness=-1.1706578293858168, kurtosis=6.447553720460917)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(11,)\n" - ] - } - ], - "source": [ - "import tables\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import os\n", - "from ctapipe.visualization import CameraDisplay\n", - "from ctapipe.instrument import CameraGeometry\n", - "from ctapipe.image.cleaning import tailcuts_clean\n", - "from ctapipe.image import hillas_parameters\n", - "import matplotlib as mpl\n", - "%matplotlib inline\n", - "\n", - "#Reading data from the hdf5 file. Just to make this script work, 10 MAGIC events are stored\n", - "#in this file. \n", - "data = tables.open_file(\"M1_magic_data.h5\")\n", - "\n", - "\n", - "t1 = data.root\n", - "print(t1.MAGIC)\n", - "row_str = t1.MAGIC\n", - "table = row_str\n", - "\n", - "print(repr(table))\n", - "\n", - "arrtime_image=[x['arrival_time'] for x in table.iterrows()]\n", - "charge_image=[x['image_charge'] for x in table.iterrows()]\n", - "event_mjd = [x['event_time'] for x in table.iterrows()]\n", - "pointings=[x['pointing'] for x in table.iterrows()]\n", - "\n", - "#print(event_mjd)\n", - "\n", - "from ctapipe.io.containers import DL1Container\n", - "\n", - "dl1=DL1Container()\n", - "\n", - "dl1.tel[0].image = charge_image[2] # this corrssponds to 3rd event in the file\n", - "\n", - "image = dl1.tel[0].image\n", - "print(\"DEBUG\")\n", - "#print(im)\n", - "print(image.shape)\n", - "\n", - "cleaned_image = image\n", - "\n", - "\n", - "#MAGICCam geometry file is still not a part of ctapipe yet. Hence the file need to be stored \n", - "# in the same directory where this script will be present. Here the name of the CameraGeometry file \n", - "#is MAGICCam.fits.gz\n", - "\n", - "camgeom = CameraGeometry.from_name(\"MAGICCam\")\n", - "#camgeom = CameraGeometry.from_name(\"MAGIC\")\n", - "mask = tailcuts_clean(camgeom,image,picture_thresh=7,boundary_thresh=5,keep_isolated_pixels=False)\n", - "cleaned_image[~mask] = 0\n", - "ind=np.where(cleaned_image !=0.0)\n", - "print(cleaned_image[ind])\n", - "#print(ind)\n", - "\n", - "size = np.sum(cleaned_image)\n", - "\n", - "try:\n", - " hillas_params = hillas_parameters(camgeom,cleaned_image)\n", - "except:\n", - " pass\n", - "#\n", - "plt.figure(figsize=(20,16))\n", - "disp = CameraDisplay(camgeom)\n", - "disp.image = cleaned_image\n", - "disp.add_colorbar()\n", - "disp.overlay_moments(hillas_params, color='cyan', linewidth=3)\n", - "print(hillas_params)\n", - "mpl.rcParams['savefig.dpi'] = 120\n", - "mpl.rcParams[\"figure.dpi\"] = 120\n", - "#plt.figure(figsize=(20,20))\n", - "plt.savefig(\"An_evt_magic_cam_new.png\",bbox_inches='tight')\n", - "plt.show()\n", - "\n", - "print(np.shape(event_mjd)) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/lstchain/magic-lst/M1_magic_data.h5 b/lstchain/magic-lst/M1_magic_data.h5 deleted file mode 100644 index 9defb1055518e1931a35870532b7d337c79f3b9b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 134128 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a/lstchain/magic-lst/MAGIC_Camera_Geometry.ipynb +++ /dev/null @@ -1,286 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MAGIC Camera Geometry display using CTAPIPE\n", - "\n", - "We take MAGIC camera in MAGIC coordinate system and just display it using ctapipe" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from subprocess import call\n", - "import os\n", - "os.system(\"export CTAPIPE_SVC_PATH=$CTAPIPE_SVC_PATH:$PWD\")\n", - "os.system(\"echo $CTAPIPE_SVC_PATH\")\n", - "# Change the path \"DIR_OF_THIS_SCRIPT\" \n", - "os.environ[\"CTAPIPE_SVC_PATH\"]=\"/photon1/users/labsaha/Dropbox/ctapipe\"\n", - "\n", - "from ctapipe.visualization import CameraDisplay\n", - "from ctapipe.instrument import CameraGeometry\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Display of MAGIC Camera in CTAPIPE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "** This is to understand if the camera is appearing in CTAPIPE display as expected **\n", - "\n", - "# STEP 1 \n", - "**(MAGIC Camera information without changing the frame of reference, .i.e. what we see in MARS)**\n", - "\n", - "An angle of rotation 19.1 is given all pixels\n", - "\n", - "The angle of rotation of the pixels are given though ctapipe." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead. [__main__]\n", - "WARNING:astropy:AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead.\n", - "/photon1/users/labsaha/miniconda/envs/cta-dev/lib/python3.6/site-packages/matplotlib/figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", - " \"matplotlib is currently using a non-GUI backend, \"\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from astropy.io import fits\n", - "import astropy.units as u\n", - "f = open('./magic_camera_info.txt', 'r')\n", - "\n", - "x=[]\n", - "y=[]\n", - "j=[]\n", - "A=[]\n", - "for line in f:\n", - " line = line.strip()\n", - " columns = line.split()\n", - " j.append(int(columns[0]))\n", - " x.append(float(columns[1]))\n", - " y.append(float(columns[2]))\n", - " A.append(float(columns[3]))\n", - "\n", - "angle = 19.1 #.11460397105\n", - "\n", - "x1 = np.array(x)\n", - "y1 = np.array(y)\n", - "\n", - "A = 7.79422851562500000e+02*1.0e-6 \n", - "#x = x/1.0e-3\n", - "#y= y/1.0e-3\n", - "Area=np.repeat(A, 1039)\n", - "#print(y)\n", - "f.close()\n", - "\n", - "#for i in range(100):\n", - "# print(x2[i],y2[i])\n", - "\n", - "header= fits.Header()\n", - "\n", - "\n", - "c1 = fits.Column(name='pix_id', array=np.array(j), format='K')\n", - "c2 = fits.Column(name='pix_x', array=np.array(x1)*1.0e-3, unit='m', format='D')\n", - "c3 = fits.Column(name='pix_y', array=np.array(y1)*1.0e-3, unit='m', format='D')\n", - "c4 = fits.Column(name='pix_area', array=np.array(Area), unit='m^2', format='D')\n", - "t = fits.BinTableHDU.from_columns([c1, c2, c3, c4])\n", - "\n", - "t.header['PIX_TYPE'] = 'hexagonal'\n", - "t.header['CAM_ID'] = 'MAGICCam'\n", - "t.header['CAM_ROT'] = 0.0\n", - "t.header['PIX_ROT'] = +angle\n", - "\n", - "t.writeto('MAGICCam_1.camgeom.fits',clobber=True)\n", - "\n", - "plt.figure(figsize=(20,16))\n", - "camgeom = CameraGeometry.from_name(\"MAGICCam_1\")\n", - "disp = CameraDisplay(camgeom)\n", - "disp.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# STEP 2\n", - "**(MAGIC Camera information changing the frame of reference to HESS.)**\n", - "\n", - "In the HESS reference frame following transfomation should be given\n", - "\n", - "xHess = -yMagic\n", - "\n", - "yHess = -xMagic\n", - "\n", - "\n", - "As we know that an angle of rotation +19.1 should be given to all pixels to make the proper allignment of \n", - "the pixels, we should provide the angle accordingly in the HESS frame. In the HESS frame the pixel \n", - "rotation is not just simply 19.1 deg, it is **90-19.1** deg. We give the rotation of pixels through\n", - "**ctapipe** \n", - "\n", - "Once can manually give the pixel rotation using angle **-(90.0-19.1)** and then the result will be \n", - "the same. \n", - "\n", - "The camera geometry file produced by this transformation should be used while using for ctapipe." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead. [__main__]\n", - "WARNING:astropy:AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead.\n", - "/photon1/users/labsaha/miniconda/envs/cta-dev/lib/python3.6/site-packages/matplotlib/figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", - " \"matplotlib is currently using a non-GUI backend, \"\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from astropy.io import fits\n", - "import astropy.units as u\n", - "f = open('./magic_camera_info.txt', 'r')\n", - "\n", - "x=[]\n", - "y=[]\n", - "j=[]\n", - "A=[]\n", - "for line in f:\n", - " line = line.strip()\n", - " columns = line.split()\n", - " j.append(int(columns[0]))\n", - " x.append(float(columns[1]))\n", - " y.append(float(columns[2]))\n", - " A.append(float(columns[3]))\n", - "\n", - "angle = 19.1 #.11460397105\n", - "\n", - "x1 = np.array(x)\n", - "y1 = np.array(y)\n", - "\n", - "\n", - "\n", - "\n", - "xMagic2Hess = -y1\n", - "yMagic2Hess = -x1\n", - "\n", - "#x2 = -y1\n", - "#y2 = -x1\n", - "\n", - "#x3 = np.cos(+angle*u.deg)*x2 - np.sin(+angle*u.deg)*y2\n", - "#y3 = np.cos(+angle*u.deg)*y2 + np.sin(+angle*u.deg)*x2\n", - "\n", - "\n", - "A = 7.79422851562500000e+02*1.0e-6 \n", - "#x = x/1.0e-3\n", - "#y= y/1.0e-3\n", - "Area=np.repeat(A, 1039)\n", - "#print(y)\n", - "f.close()\n", - "\n", - "#for i in range(100):\n", - "# print(x2[i],y2[i])\n", - "\n", - "header= fits.Header()\n", - "\n", - "\n", - "c1 = fits.Column(name='pix_id', array=np.array(j), format='K')\n", - "c2 = fits.Column(name='pix_x', array=np.array(xMagic2Hess)*1.0e-3, unit='m', format='D')\n", - "c3 = fits.Column(name='pix_y', array=np.array(yMagic2Hess)*1.0e-3, unit='m', format='D')\n", - "#c2 = fits.Column(name='pix_x', array=np.array(x3)*1.0e-3, unit='m', format='D')\n", - "#c3 = fits.Column(name='pix_y', array=np.array(y3)*1.0e-3, unit='m', format='D')\n", - "c4 = fits.Column(name='pix_area', array=np.array(Area), unit='m^2', format='D')\n", - "c4 = fits.Column(name='pix_area', array=np.array(Area), unit='m^2', format='D')\n", - "t = fits.BinTableHDU.from_columns([c1, c2, c3, c4])\n", - "\n", - "t.header['PIX_TYPE'] = 'hexagonal'\n", - "t.header['CAM_ID'] = 'MAGICCam'\n", - "t.header['CAM_ROT'] = 0.0\n", - "t.header['PIX_ROT'] = 90.0-angle\n", - "\n", - "t.writeto('MAGICCam_2.camgeom.fits',clobber=True)\n", - "\n", - "plt.figure(figsize=(20,16))\n", - "camgeom = CameraGeometry.from_name(\"MAGICCam_2\")\n", - "disp = CameraDisplay(camgeom)\n", - "disp.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/lstchain/magic-lst/MAGIC_LST1_analysis.pdf b/lstchain/magic-lst/MAGIC_LST1_analysis.pdf deleted file mode 100644 index 5a42f620f80fbdbb02e15b2614562ac87388e6e7..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 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All information from MAGIC root files are not imported\n", - "to DL1 HDF5 file. Only the required information are stored in the file." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "import sys\n", - "import ROOT\n", - "from ROOT import gSystem,AddressOf\n", - "gSystem.Load(\"/remote/gamma3/users/labsaha/Mars_V2-18-1/libmars.so\")\n", - "import ROOT\n", - "import tables" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('Total number of events: ', 13332L)\n", - "('Total number of events: ', 13332L)\n" - ] - } - ], - "source": [ - "# Create h5 file for data dumping\n", - "f = tables.open_file('M1_magic_data_new.h5',mode=\"a\", title=\"Output File\")\n", - "\n", - "attributes = f.root._v_attrs\n", - "\n", - "tel_type='M1'\n", - "description='MAGIC Telescope'\n", - "tel_types={'MAGIC':'MAGIC-Came'}\n", - "tel_type='MAGIC'\n", - "img_dtypes={'MAGIC':'float16',}\n", - "\n", - "#--------------------------------\n", - "\n", - "# Reading the calibrated MAGIC file\n", - "# Provide path of the root file which is modified using the macro \"\"\n", - "\n", - "ch = ROOT.TChain(\"Events\");\n", - "#ch.Add(\"../../data_ctapipe/out/*M1*05052758*_Y_*\");\n", - "ch.Add(\"../../data_ctapipe/out/20160428_M1_05052758.001_Y_CrabNebula-W0.40+215.root\");\n", - "# Getting 'Event' Tree\n", - "ch.SetBranchStatus(\"*\", 0);\n", - "ch.SetBranchStatus(\"MCerPhotEvt.*\", 1)\n", - "ch.SetBranchStatus(\"MArrivalTime.*\", 1)\n", - "ch.SetBranchStatus(\"MRawEvtHeader.*\", 1)\n", - "ch.SetBranchStatus(\"MPointingPos.*\", 1)\n", - "ch.SetBranchStatus(\"MTime.*\",1)\n", - "\n", - "\n", - "total_nr_of_events = ch.GetEntries()\n", - "\n", - "print(\"Total number of events: \",total_nr_of_events)\n", - "im=[]\n", - "artime=[]\n", - "trigger_number=[]\n", - "pointing=[]\n", - "pointing_pos_Zd=[]\n", - "pointing_pos_Az=[]\n", - "Mtime=[]\n", - "index = 0\n", - "\n", - "for i, event in zip(range(total_nr_of_events),ch):\n", - " event.GetEntry(i)\n", - " camera_trigger=getattr(event, \"MRawEvtHeader.\")\n", - " \n", - " camera_event=getattr(event, \"MCerPhotEvt.\")\n", - " camera_time=getattr(event, \"MArrivalTime.\")\n", - " pointingpos=getattr(event, \"MPointingPos.\")\n", - " mtime = getattr(event, \"MTime.\")\n", - " n_pix=camera_event.GetNumPixels()\n", - "# print('npixel',n_pix)\n", - " im.append([])\n", - " artime.append([])\n", - "# trigger_number.append([])\n", - "# pointing_pos_Zd.append([])\n", - "# pointing_pos_Az.append([])\n", - " for j in range(1,1039+1):\n", - " nphot = camera_event.GetPixById(j).GetNumPhotons()\n", - " pix_id = camera_event.GetPixById(j).GetPixId()\n", - "# pix_x = camera_event.GetPixById(j).GetX() \n", - " im[index].append(nphot)\n", - "# print(j) \n", - " artime[index].append(camera_time[j])\n", - " \n", - " index += 1\n", - " \n", - " trigger_number.append(camera_trigger.GetDAQEvtNumber())\n", - " pointing_pos_Zd.append(pointingpos.GetZd())\n", - " pointing_pos_Az.append(pointingpos.GetAz())\n", - " pointing.append([pointingpos.GetZd(),pointingpos.GetAz()])\n", - " Mtime.append(mtime.GetMjd())\n", - "\n", - "array_shape = (1039,)\n", - "np_type = np.dtype((np.dtype(img_dtypes[tel_type]), array_shape))\n", - "np_type1 = np.dtype((np.dtype(img_dtypes[tel_type]), (2,)))\n", - "columns_dict={\"event_index\":tables.Int16Col(),\"trigger_number\":tables.Int16Col(),\n", - " \"event_time\":tables.Float32Col(),\n", - " \"arrival_time\":tables.Col.from_dtype(np_type),\"image_charge\":tables.Col.from_dtype(np_type),\n", - " \"pointing\":tables.Col.from_dtype(np_type1)}\n", - "#columns_dict[\"arrival_time\"] = tables.Col.from_dtype(np_type)\n", - "#columns_dict[\"image_charge\"]=tables.Col.from_dtype(np_type)\n", - "#columns_dict[\"pointing_Zd\"] = tables.Float32Col()\n", - "#columns_dict[\"pointing_Az\"] = tables.Float32Col()\n", - "#columns_dict[\"pointing\"] = tables.Col.from_dtype(np_type1)\n", - "\n", - "\n", - "description = type('description', (tables.IsDescription,), columns_dict)\n", - "\n", - "\n", - "# Writting the information in the hdf5 file\n", - "table = f.create_table(f.root,tel_type,description,\"Table of {} images\".format(tel_type))\n", - "\n", - "image_row = table.row\n", - "\n", - "\n", - "shape = 1039 # number of MAGIC pixels\n", - "image_row['event_index'] = -1\n", - "image_row['trigger_number'] = 0.\n", - "image_row['event_time'] = 0.0\n", - "image_row['image_charge'] = np.zeros(shape,dtype=img_dtypes[tel_type])\n", - "\n", - "image_row['arrival_time'] = np.zeros(shape,dtype=img_dtypes[tel_type])\n", - "image_row['pointing']= np.zeros(2,dtype=img_dtypes[tel_type])\n", - "#image_row['pointing_Zd'] = 0.\n", - "#image_row['pointing_Az'] = 0.\n", - "image_row.append()\n", - "table.flush()\n", - "\n", - "for i in range(total_nr_of_events):\n", - " table = eval('f.root.{}'.format(tel_type))\n", - " image_row = table.row\n", - "\n", - "#pixel_vector = event.dl1.tel[tel_id].image[0]\n", - " image_row[\"event_index\"] = i\n", - " image_row['trigger_number'] = trigger_number[i]\n", - " image_row['event_time'] = Mtime[i]-int(Mtime[i])\n", - " image_row['image_charge'] = im[i]\n", - " image_row['arrival_time'] = artime[i]\n", - " image_row['pointing']=pointing[i]\n", - "# image_row['pointing_Zd'] = pointing_pos_Zd[i]\n", - "# image_row['pointing_Az'] = pointing_pos_Az[i]\n", - "\n", - " image_row.append()\n", - " # index_vector.append(next_index)\n", - " table.flush()\n", - "\n", - "\n", - "\n", - "\n", - "f.close()\n", - "\n", - "#h5f.close()\n", - "total_nr_of_events = ch.GetEntries()\n", - "\n", - "print(\"Total number of events: \",total_nr_of_events)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/lstchain/magic-lst/README.md b/lstchain/magic-lst/README.md deleted file mode 100644 index 4ae7437220..0000000000 --- a/lstchain/magic-lst/README.md +++ /dev/null @@ -1,16 +0,0 @@ -This directory is for keeping the development version of analysis code for MAGIC and LST joint data analysis - -In order to feed MAGIC data in ctapipe we need to convert MAGIC DL1 data to a specific format. Currently we are converting MAGIC DL1 root files to HDF5 format. The data being stored in the HDF5 files are: 1) image charge, 2) photon arraival time in each pixel, 3) stereo trigger number, 4) arrival time of the event, 5) pointing positions of the telescope - -The convertions of the MAGIC DL1 root files are done in two steps - -# Step 1: -Addding pointing position container to MAGIC DL1 root files - -# step 2: -1. Converting the files obtained in "Step 1" to HDF5 format -2. Then remove the files created in step 1. - -Further details: - -Since MAGIC DL1 root files doesn't contain the pointing information for all events, we need to add first that information to the root files. Then we can convert the file to HDF5 format. One can skip step one and do every thing in one step. However, one may need to work a bit more to do so. Since it was easier for us to proceed with these two-step procedure, we will move ahead with this. Any smart changes is always welcome! diff --git a/lstchain/magic-lst/add_pointingpos.cc b/lstchain/magic-lst/add_pointingpos.cc deleted file mode 100644 index b007a4149e..0000000000 --- a/lstchain/magic-lst/add_pointingpos.cc +++ /dev/null @@ -1,138 +0,0 @@ -//======================= -/* This is to add PointingPos container to the _Y_ files - Author(s): David Fidalgo, Lab Saha -*/ -//======================== -#include "MReadMarsFile.h" -#include "MParList.h" -#include "MTaskList.h" -#include "MWriteRootFile.h" -#include "MEvtLoop.h" - -#include "MPointingPos.h" -#include "MPointingPosCalc.h" -#include "MPointingPosInterpolate.h" -#include "MObservatory.h" -#include "MStarguiderCalibration.h" -#include "MReadReports.h" - -#include "TString.h" - -#include - - -int add_pointingpos(const char* datafile,const char* outdir) { -//int main(char * datafile[]) { - TString input = datafile; -// Edit the path of the output directory -// TString outputDirectory = "../../data_ctapipe/out"; - TString outputDirectory = outdir; - Bool_t IsUseStarguider = kTRUE; - - - // read in everything - MReadReports readreal; - - readreal.AddTree("Events", "MTime.", kTRUE); - readreal.AddTree("Drive"); - readreal.AddTree("Pedestals"); - readreal.AddTree("CC"); - readreal.AddTree("Pyrometer"); - readreal.AddTree("Starguider"); - readreal.AddTree("Trigger"); - readreal.AddTree("Runbook"); - readreal.AddTree("AMC"); - readreal.AddTree("DT"); - readreal.AddTree("DAQ"); - readreal.AddTree("SUMO"); - readreal.AddTree("Camera"); - readreal.AddTree("Lidar") ; - readreal.AddTree("Laser"); - readreal.AddTree("Weather"); - readreal.AddTree("SumTrigger"); - readreal.AddTree("L3T"); - - readreal.AddFile(input.Data()); - - - // Parameter list: contains all parameter containers (input/output) - // The reader task will automatically put in the parameter list the - // containers which are read-in. - MParList parlist; - - // Task list: ordered list of all the tasks to be performed in the event loop - MTaskList tasklist; - - // The task list itself has to be put into the parameter list: - parlist.AddToList(&tasklist); - - MStarguiderCalibration fCalibStar; - if (IsUseStarguider) - parlist.AddToList(&fCalibStar); - - // Now add the reading task to the task list: - tasklist.AddToList(&readreal); - - MPointingPos pos; - parlist.AddToList(&pos); - MObservatory obs; - parlist.AddToList(&obs); - - MPointingPosInterpolate pextr; - - pextr.AddFile(input.Data()); - pextr.SetUseStarguider(IsUseStarguider); - tasklist.AddToList(&pextr); - - // Now a task to write out an output file, containing the usual MARS trees (Events, RunHeaders) but - // with only the data we want inside. - // First argument of constructor is compression level (see root Manual). - // The second argument is the "rule" to obtain the name of the output file from the name of the input file. - // Usually the name is kept unchanged except for the "tag" between underscores, for instance *_I_*.root ==> *_Q_*.root - // when we run melibea over star files. In this example we replace the _I_ of star files by _H_ - // Before the "{" in the second argument we set the output directory - // The third argument means the output files will be overwritten if already existing. - MWriteRootFile write(2, Form("%s{s/_Y_/_Y_}", outputDirectory.Data()), "RECREATE"); - - // Now add the containers you want to keep in the output, specifying the tree to which they have to be added (2nd argument). - // The containers may be either newly created ones or containers already existing in the input file (which will be - // present in the parameter list) - write.AddCopySource("Events"); - write.AddContainer("MPointingPos", "Events"); - write.AddContainer("MRawEvtHeader", "Events"); - write.AddContainer("MTime", "Events"); - write.AddContainer("MCerPhotEvt", "Events"); - write.AddContainer("MTriggerPattern", "Events"); - write.AddContainer("MArrivalTime", "Events"); - - // NOTE: in the containers, like MHillas in this case, which are simply "copied" to the output from the input file, - // only the enabled branches (see "read" task at beginning of code) will be filled. The rest will just contain the - // default values! - - // Now add the writing task to the tasklist: - tasklist.AddToList(&write); - - // The Mars event loop. One has to specify which is its parameter list: - MEvtLoop evtloop; - evtloop.SetParList(&parlist); - - // - // We execute the loop: - // - // - The PreProcess function of All the tasks in the tasklist will be executed before doing anything else - // - Then the Process function of all tasks is executed for each event in the Events tree of the Mars files, - // except if a filter avoids it - // - The ReInit function (if it exists) of each task is executed every time a new Mars file is opened. This - // is needed because some parameters are written in the RunHeaders tree (which has one entry per file) and - // hence they are read every time a new file is opened. So in the ReInit function of a task we can update - // data members which depend on information held in RunHeaders - // - At the end of the loop the PostProcess function of all tasks in the task list is called. - // - if (!evtloop.Eventloop()) - return -1; - - // Print statistics of the event loop: - tasklist.PrintStatistics(); - - return 0; -} diff --git a/lstchain/magic-lst/magic_camera_info.txt b/lstchain/magic-lst/magic_camera_info.txt deleted file mode 100644 index 5e42e54ce3..0000000000 --- a/lstchain/magic-lst/magic_camera_info.txt +++ /dev/null @@ -1,1039 +0,0 @@ -0 0.000000 0.000000 779.422852 -1 28.346991 9.819690 779.422852 -2 5.669400 29.459061 779.422852 -3 -22.677601 19.639370 779.422852 -4 -28.346991 -9.819690 779.422852 -5 -5.669400 -29.459061 779.422852 -6 22.677601 -19.639370 779.422852 -7 56.693989 19.639370 779.422852 -8 34.016392 39.278751 779.422852 -9 11.338800 58.918121 779.422852 -10 -17.008200 49.098431 779.422852 -11 -45.355190 39.278751 779.422852 -12 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