diff --git a/cal_tools/cal_tools/ana_tools.py b/cal_tools/cal_tools/ana_tools.py index b84b23b06d5d2b1e7160224d6c59ec7663101403..cfcbcd9ff77c56b5aae4f835e766e3976850ff00 100644 --- a/cal_tools/cal_tools/ana_tools.py +++ b/cal_tools/cal_tools/ana_tools.py @@ -125,7 +125,6 @@ def recursively_save_dict_contents_to_group(h5file, path, dic): else: raise ValueError('Cannot save a list of %s ' % type(item)) - # Save strings, scalars or array of scalars elif (np.isscalar(item) or isinstance(item, (str, np.ndarray))): @@ -321,7 +320,7 @@ def hm_combine(data, fname=None, htype=None, **kwargs): :param data: 2D numpy.array of data points to plot :param fname: file name of output file - :param type: type of figure + :param htype: type of figure :param kwargs: other keywords supported by pyDetLib heatmapPlot """ diff --git a/notebooks/AGIPD/PlotFromCalDB_AGIPD_NBC.ipynb b/notebooks/AGIPD/PlotFromCalDB_AGIPD_NBC.ipynb index d840128138e02aa57ab271ab9d8772d76e9707bd..f36c345ea00d5deeb8df4822b9cf1349cbf8230a 100644 --- a/notebooks/AGIPD/PlotFromCalDB_AGIPD_NBC.ipynb +++ b/notebooks/AGIPD/PlotFromCalDB_AGIPD_NBC.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": true }, @@ -37,17 +37,17 @@ "photon_energy = 9.2 # Photon energy of the beam\n", "out_folder = \"/gpfs/exfel/data/scratch/karnem/testAGIPD16_10/\" # Output folder, required\n", "use_existing = \"/gpfs/exfel/data/scratch/karnem/testAGIPD16_10/\" # Input folder\n", - "cal_db_timeout = 1200000 # timeout on caldb requests\",\n", + "cal_db_timeout = 180000 # timeout on caldb requests\",\n", "adu_to_photon = 8000 / 3.6 / 67.# ADU to photon conversion factor\n", "nMemToShow = 32 # Number of memory cells to be shown in plots over ASICs\n", - "detectorDB = \"AGIPD1M1\" # detector entry in the DB to investigate\n", + "db_module = \"AGIPD1M1\" # detector entry in the DB to investigate\n", "dclass = \"AGIPD\" # Detector class\n", "cal_db_interface = \"tcp://max-exfl016:8015#8025\" # the database interface to use" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": false, "scrolled": true @@ -74,20 +74,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bad pixels data: {'SlopesFF': 'BadPixelsFF', 'Noise': 'BadPixelsDark', 'SlopesPC': 'BadPixelsPC', 'Offset': 'BadPixelsDark'}\n", - "CalDB Interface tcp://max-exfl016:8023\n" - ] - } - ], + "outputs": [], "source": [ "# Prepare variables\n", "interval = 1 # interval for evaluation in days\n", @@ -117,7 +108,7 @@ "os.makedirs(out_folder, exist_ok=True)\n", "\n", "# Get getector conditions\n", - "det = getattr(Detectors, detectorDB)\n", + "det = getattr(Detectors, db_module)\n", "dconstants = getattr(Constants, dclass)\n", "\n", "cal_db_interface = get_random_db_interface(cal_db_interface)\n", @@ -126,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false }, @@ -246,7 +237,7 @@ "\n", " # Request bad pixel mask\n", " if const in constantsDark:\n", - " print('RequestBP ',\n", + " print('Request bad pixels ',\n", " (creation_time + step + step + step + step))\n", " cdataBP, ctimeBP = get_from_db(getattr(det, qm),\n", " getattr(dconstants,\n", @@ -267,7 +258,7 @@ " dt = ctime - step\n", " continue\n", "\n", - " print(\"Found BP {}\".format(ctimeBP))\n", + " print(\"Found bad pixels {}\".format(ctimeBP))\n", " print(cdataBP.shape)\n", " cdataBP = modify_const(const, cdataBP, True)\n", " print(cdataBP.shape)\n", @@ -319,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": false, "scrolled": true @@ -334,20 +325,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load data from /gpfs/exfel/data/scratch/karnem/testAGIPD16_10//CalDBAna_AGIPD_*.h5\n" - ] - } - ], + "outputs": [], "source": [ "if use_existing == \"\":\n", " fpath = '{}/CalDBAna_{}_*.h5'.format(out_folder, dclass)\n", @@ -360,65 +343,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculate Gain and Noise in electron units\n", - "Q1M1\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q1M2\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q1M3\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q1M4\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q2M1\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q2M2\n", - "Q2M3\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q2M4\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q3M1\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q3M2\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q3M3\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q3M4\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q4M1\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q4M2\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q4M3\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n", - "Q4M4\n", - "(3, 2, 8, 176)\n", - "(3, 2, 8, 176)\n" - ] - } - ], + "outputs": [], "source": [ "# Combine FF and PC data to calculate Gain\n", "# Estimate Noise in units of electrons\n", @@ -514,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": true }, @@ -547,83 +476,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plot calibration constants\n", - "Const: Gain, gain 0\n", - "Q1M1\n", - "Const: Gain, gain 1\n" - ] - }, - { - "data": { - "image/png": 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IIGmvUFUXJDlxkYc8ATi3qgr4WJLDk9ylqq5YlwAHmk0M2dBj+qht445Ckpo2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\ntSQ9HriKqqsBSL4MPB/YcWLYxKhkTelsYji+2wS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57m8FXgg8cY77h1XVtR3063YcSpYkSepYVZ1NE/zNdf+aqjoPuHXlevXLzBhKkqSxVWS5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\n+2cBByc5GKCq/mWhCgwMpa4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D6p8jgPfXv8eBV9YJ5Abg4oj4Qs++f5uZ72r7PtiuIUmSRlZS9Vlf6E8DhwNX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\n7gl8t6HXlCRJUkMaqfpV1V9tY/vFST7XxGtKkiT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yy+3+A5N8Msk5Sc5O8oyRa16e5Kvt+f+SZI8lyt55wa4XAX8GPBP4hz71N2iU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\nY7Fz9gcuXqbcZwP/nuQVNMnAw1da8b4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JalI6j2G7shNDqSR+UVGJ/ECT1EU2JUuSJAmwYihJkrZgiU3J7YpODNMXSpIk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\nWOuAg5McRC8hfANw7LRjbgReBlyaZA3wNOCWJI8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vA9snudfS1G4Tm5Klrjj7gYaB6QMNuaLvjN9sdkqytmf/hKo6YQ737wpc2bO/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\nBX2v11TVYc3C7TdU1cEDDE/qHCuG7dmc5OgkxwJ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- "text/plain": [ - "<matplotlib.figure.Figure at 0x2b68f5aadeb8>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "print('Plot calibration constants')\n", "\n", @@ -755,8 +613,7 @@ " title=title, cb_label=cb_label,\n", " fname='{}/{}_{}_g{}_MEM_{}.png'.format(\n", " out_folder, const, mod, gain, key),\n", - " vmin=vmin, vmax=vmax)\n", - " break" + " vmin=vmin, vmax=vmax)" ] } ], diff --git a/notebooks/AGIPD/PlotFromCalDB_Summary_AGIPD_NBC.ipynb b/notebooks/AGIPD/PlotFromCalDB_Summary_AGIPD_NBC.ipynb index 23db813cc49ef042bb56f1975e278aed642cc1c3..2c3c3a5f64984a3f3c5b885ddc9fc46d53eaf0b4 100644 --- a/notebooks/AGIPD/PlotFromCalDB_Summary_AGIPD_NBC.ipynb +++ b/notebooks/AGIPD/PlotFromCalDB_Summary_AGIPD_NBC.ipynb @@ -20,9 +20,10 @@ "outputs": [], "source": [ "cluster_profile = \"noDB\" # The ipcluster profile to use\n", - "out_folder = \"/gpfs/exfel/data/scratch/karnem/testAGIPD16_14/\" # Output folder, required\n", - "use_existing = \"/gpfs/exfel/data/scratch/karnem/testAGIPD16_21/\" # Input folder\n", - "dclass = \"AGIPD\" # Detector class" + "out_folder = \"/gpfs/exfel/data/scratch/karnem/testLPD_11/\" # Output folder, required\n", + "use_existing = \"/gpfs/exfel/data/scratch/karnem/testLPD_10/\" # Input folder\n", + "dclass = \"LPD\" # Detector class\n", + "nMemToShow = 32 # Number of memory cells to be shown in plots over ASICs" ] }, { @@ -41,7 +42,9 @@ "import matplotlib\n", "% matplotlib inline\n", "\n", - "from cal_tools.ana_tools import *" + "from cal_tools.ana_tools import (load_data_from_hdf5, \n", + " HMType, multi_union,\n", + " hm_combine)" ] }, { @@ -95,8 +98,6 @@ " \"SlopesFF\": [[0.8, 1.2], [0.6, 1.2]]\n", "}\n", "\n", - "nMemToShow = 32\n", - "\n", "keys = {\n", " 'Mean': ['data', '', 'Mean over pixels'],\n", " 'std': ['dataStd', '', '$\\sigma$ over pixels'],\n", @@ -226,10 +227,9 @@ " vmax = np.nanmean(\n", " np.array(mod_data[key])) + 2*np.nanstd(np.array(mod_data[key]))\n", "\n", + " htype = None\n", " if const in ['SlopesFF', 'SlopesPC', 'SlopesCI']:\n", - " htype = 2\n", - " else:\n", - " htype = 0\n", + " htype = HMType.INSET_1D\n", "\n", " if key == 'NBP':\n", " unit = '[%]'\n", @@ -252,7 +252,7 @@ " fname='{}/{}_all_g{}_{}.png'.format(\n", " out_folder, const, gain, key),\n", " vmin=vmin, vmax=vmax,\n", - " pad=[0.125, 0.151, 0.12, 0.17], type=htype)" + " pad=[0.125, 0.151, 0.12, 0.17], htype=htype)" ] } ], diff --git a/notebooks/LPD/PlotFromCalDB_LPD_NBC.ipynb b/notebooks/LPD/PlotFromCalDB_LPD_NBC.ipynb index f70f9136513c6faf99ab67973d04e401caf66e8d..f540215d90303c6c05aa8df15633a8339a3e2a6a 100644 --- a/notebooks/LPD/PlotFromCalDB_LPD_NBC.ipynb +++ b/notebooks/LPD/PlotFromCalDB_LPD_NBC.ipynb @@ -38,7 +38,9 @@ "out_folder = \"/gpfs/exfel/data/scratch/karnem/testLPD_11/\" # Output folder, required\n", "use_existing = \"/gpfs/exfel/data/scratch/karnem/testLPD_10/\" # Input folder\n", "cal_db_timeout = 180000 # timeout on caldb requests\",\n", - "detectorDB = \"LPD1M1\" # detector entry in the DB to investigate\n", + "adu_to_photon = 8000 / 3.6 / 67.# ADU to photon conversion factor\n", + "nMemToShow = 32 # Number of memory cells to be shown in plots over ASICs\n", + "db_module = \"LPD1M1\" # detector entry in the DB to investigate\n", "dclass = \"LPD\" # Detector class\n", "cal_db_interface = \"tcp://max-exfl016:8015#8025\" # the database interface to use" ] @@ -64,8 +66,10 @@ "% matplotlib inline\n", "\n", "from iCalibrationDB import Constants, Conditions, Detectors\n", - "from cal_tools.tools import get_from_db\n", - "from cal_tools.ana_tools import *" + "from cal_tools.tools import get_from_db, get_random_db_interface\n", + "from cal_tools.ana_tools import (save_dict_to_hdf5, load_data_from_hdf5, \n", + " combine_constants, HMType,\n", + " hm_combine)" ] }, { @@ -79,11 +83,9 @@ "# Prepare variables\n", "interval = 1 # interval for evaluation in days\n", "\n", - "nMem = 512 # Number of mem Cells to store\n", + "nMem = max(mem_cells) # Number of mem Cells to store\n", "spShape = (64,64) # Shape of superpixel\n", "\n", - "adu_to_photon = 8000 / 3.6 / 67.# ADU to photon conversion factor\n", - "\n", "in_mod_names = []\n", "for mod in modules:\n", " in_mod_names.append(\"Q{}M{}\".format(mod // 4 + 1, mod % 4 + 1))\n", @@ -106,15 +108,10 @@ "os.makedirs(out_folder, exist_ok=True)\n", "\n", "# Get getector conditions\n", - "det = getattr(Detectors, detectorDB)\n", + "det = getattr(Detectors, db_module)\n", "dconstants = getattr(Constants, dclass)\n", "\n", - "if \"#\" in cal_db_interface:\n", - " prot, serv, ran = cal_db_interface.split(\":\")\n", - " r1, r2 = ran.split(\"#\")\n", - " cal_db_interface = \":\".join(\n", - " [prot, serv, str(np.random.randint(int(r1), int(r2)))])\n", - "\n", + "cal_db_interface = get_random_db_interface(cal_db_interface)\n", "print('CalDB Interface {}'.format(cal_db_interface))" ] }, @@ -155,7 +152,7 @@ " data = data[:,:,:,:,0]\n", "\n", " if len(data.shape) < 4:\n", - " print(data.shape, \"Unexpected shape !!!!!!!!!\")\n", + " print(data.shape, \"Unexpected shape!\")\n", "\n", " if data.shape[0] != 256:\n", " data = data.swapaxes(0, 2).swapaxes(1,3).swapaxes(2,3) \n", @@ -233,7 +230,7 @@ "\n", " # Request bad pixel mask\n", " if const in constantsDark:\n", - " print('RequestBP ',\n", + " print('Request bad pixels ',\n", " (creation_time + step + step + step + step))\n", " cdataBP, ctimeBP = get_from_db(getattr(det, qm),\n", " getattr(dconstants,\n", @@ -249,18 +246,18 @@ " ctimeBP = ctimeBP.calibration_constant_version.begin_at\n", "\n", " if cdataBP is None:\n", - " print(\"Bad pixels are not found !!!!!!!!!\")\n", + " print(\"Bad pixels are not found!\")\n", " ctime = ctime.replace(tzinfo=None)\n", " dt = ctime - step\n", " continue\n", "\n", - " print(\"Found BP {}\".format(ctimeBP))\n", + " print(\"Found bad pixels {}\".format(ctimeBP))\n", " print(cdataBP.shape)\n", " cdataBP = modify_const(const, cdataBP)\n", " print(cdataBP.shape)\n", "\n", " if cdataBP.shape != cdata.shape:\n", - " print('Wrong BP shape!!!')\n", + " print('Wrong bad pixel shape!')\n", " ctime = ctime.replace(tzinfo=None)\n", " dt = ctime - step\n", " continue\n", @@ -457,8 +454,6 @@ " \"SlopesFF\": [[0.8, 1.2], [0.8, 1.2]]\n", "}\n", "\n", - "nMemToShow = 32\n", - "\n", "keys = {\n", " 'Mean': ['data', '', 'Mean over pixels'],\n", " 'std': ['dataStd', '', '$\\sigma$ over pixels'],\n", @@ -593,7 +588,7 @@ " const, mod, gain_name[gain], keys[key][1])\n", " cb_label = '{}, {} {}'.format(const, keys[key][2], unit)\n", "\n", - " hm_combine(pdata[key][::-1],\n", + " hm_combine(pdata[key][::-1], htype=HMType.INSET_AXIS,\n", " x_label='Creation Time', y_label='ASIC ID',\n", " x_ticklabels=ctimes_ticks,\n", " x_ticks=np.arange(len(ctimes_ticks))+0.3,\n", @@ -605,7 +600,7 @@ " y_ticklabels=np.arange(nPixels)[::-1]+1,\n", " pad=[0.125, 0.125, 0.12, 0.185])\n", "\n", - " hm_combine(adata[key], type=0,\n", + " hm_combine(adata[key],\n", " x_label='Creation Time', y_label='Memory cell ID',\n", " x_ticklabels=ctimes_ticks,\n", " x_ticks=np.arange(len(ctimes_ticks))+0.3,\n",