From fb2a10d75c9ece33b62b0b85bb4e61531bc7a46b Mon Sep 17 00:00:00 2001
From: Karim Ahmed <karim.ahmed@xfel.eu>
Date: Wed, 22 Jul 2020 20:47:57 +0200
Subject: [PATCH] change in correction notebook
---
.../pnCCD/Characterize_pnCCD_Dark_NBC.ipynb | 35 ++--
notebooks/pnCCD/Correct_pnCCD_NBC.ipynb | 174 +++++++++++-------
2 files changed, 123 insertions(+), 86 deletions(-)
diff --git a/notebooks/pnCCD/Characterize_pnCCD_Dark_NBC.ipynb b/notebooks/pnCCD/Characterize_pnCCD_Dark_NBC.ipynb
index d06759a56..a0063669d 100644
--- a/notebooks/pnCCD/Characterize_pnCCD_Dark_NBC.ipynb
+++ b/notebooks/pnCCD/Characterize_pnCCD_Dark_NBC.ipynb
@@ -55,9 +55,9 @@
"\n",
"number_dark_frames = 0 # number of images to be used, if set to 0 all available images are used\n",
"chunkSize = 100 # number of images to read per chunk\n",
- "fix_temperature = 233. # fix temperature in K, set to 0. to use value from slow data\n",
- "gain = 1 # the detector's gain setting, only 1 and 64 is available.\n",
- "bias_voltage = 300 # detector's bias voltage\n",
+ "fix_temperature = 0. # fix temperature in K, set to 0. to use value from slow data\n",
+ "gain = 1 # the detector's gain setting, It is later read from file and this value is overwritten\n",
+ "bias_voltage = 0. # the detector's bias voltage. set to 0. to use value from slow data.\n",
"integration_time = 70 # detector's integration time\n",
"commonModeAxis = 0 # axis along which common mode will be calculated (0: along rows, 1: along columns)\n",
"commonModeBlockSize = [512, 512] # size of the detector in pixels for common mode calculations\n",
@@ -200,15 +200,15 @@
"source": [
"# extract slow data\n",
"if karabo_da_control:\n",
- "\n",
" ctrl_fname = os.path.join(ped_dir, path_template.format(run, karabo_da_control)).format(sequence)\n",
" ctrl_path = h5path_ctrl.format(karabo_id)\n",
- " mdl_ctrl_path = \"/CONTROL/{}/MDL/\".format(karabo_id)\n",
- "\n",
+ " mdl_ctrl_path = f\"/CONTROL/{karabo_id}/MDL/\"\n",
" with h5py.File(ctrl_fname, \"r\") as f:\n",
- " bias_voltage = abs(f[os.path.join(mdl_ctrl_path, \"DAQ_MPOD/u0voltage/value\")][0])\n",
+ " if bias_voltage == 0.:\n",
+ " bias_voltage = abs(f[os.path.join(mdl_ctrl_path, \"DAQ_MPOD/u0voltage/value\")][0])\n",
" gain = f[os.path.join(mdl_ctrl_path, \"DAQ_GAIN/spNCCDGain/value\")][0]\n",
- " fix_temperature = f[os.path.join(ctrl_path, \"inputA/krdg/value\")][0]"
+ " if fix_temperature == 0.:\n",
+ " fix_temperature = f[os.path.join(ctrl_path, \"inputA/krdg/value\")][0]"
]
},
{
@@ -223,7 +223,6 @@
"outputs": [],
"source": [
"# Reading Parameters such as Detector Bias, Gain, etc. from the Data:\n",
- "\n",
"memoryCells = 1 # pnCCD has 1 memory cell\n",
"sensorSize = [pixels_x, pixels_y]\n",
"blockSize = [sensorSize[0]//2, sensorSize[1]//2]# sensor area will be analysed according to blocksize\n",
@@ -240,21 +239,11 @@
"outputs": [],
"source": [
"# Printing the Parameters Read from the Data File:\n",
- "\n",
"display(Markdown('### Detector Parameters'))\n",
- "print(\"Bias voltage is {} V.\".format(bias_voltage))\n",
- "print(\"Detector gain is set to {}.\".format(gain))\n",
- "print(\"Detector integration time is set to {} ms\".format(integration_time)) \n",
- "\n",
- "if fix_temperature != 0.:\n",
- " print(f\"Using a fixed temperature of {fix_temperature} K\")\n",
- " temperature_k = fix_temperature\n",
- "else:\n",
- " print(\"Temperature is not fixed.\")\n",
- " #TODO: remove this line after properly saving the temperature in control data.\n",
- " temperature_k = 233.\n",
- " print(f\"Using a fixed temperature of {fix_temperature} K\")\n",
- " \n",
+ "print(f\"Bias voltage is {bias_voltage} V.\")\n",
+ "print(f\"Detector gain is set to {gain}.\")\n",
+ "print(f\"Detector integration time is set to {integration_time} ms\") \n",
+ "print(f\"Using a fixed temperature of {fix_temperature} K\")\n",
"print(\"Number of dark images to analyze:\", nImages) "
]
},
diff --git a/notebooks/pnCCD/Correct_pnCCD_NBC.ipynb b/notebooks/pnCCD/Correct_pnCCD_NBC.ipynb
index a41a37a6a..e592a7cf0 100644
--- a/notebooks/pnCCD/Correct_pnCCD_NBC.ipynb
+++ b/notebooks/pnCCD/Correct_pnCCD_NBC.ipynb
@@ -27,12 +27,15 @@
"run = 365 # which run to read data from\n",
"sequences = [-1] # sequences to correct, set to -1 for all, range allowed\n",
"\n",
+ "db_module = \"PnCCD1\"\n",
"karabo_da = 'PNCCD01' # data aggregators\n",
+ "karabo_da_control = \"PNCCD02\" # file inset for control data\n",
"karabo_id = \"SQS_NQS_PNCCD1MP\" # karabo prefix of PNCCD devices\n",
"receiver_id = \"PNCCD_FMT-0\" # inset for receiver devices\n",
"path_template = 'RAW-R{:04d}-PNCCD01-S{{:05d}}.h5' # the template to use to access data\n",
"path_template_seqs = \"{}/r{:04d}/*PNCCD01-S*.h5\"\n",
"h5path = '/INSTRUMENT/{}/CAL/{}:output/data/' # path to data in the HDF5 file \n",
+ "h5path_ctrl = '/CONTROL/{}/CTRL/TCTRL'\n",
"\n",
"overwrite = True # keep this as True to not overwrite the output \n",
"use_dir_creation_date = True # required to obtain creation time of the run\n",
@@ -51,9 +54,9 @@
"seq_num = 0 # sequence number for which the last plot at the end of the notebook is plotted\n",
"\n",
"# pnCCD parameters:\n",
- "fix_temperature = 233.\n",
- "gain = 1\n",
- "bias_voltage = 300\n",
+ "fix_temperature = 0. # fix temperature in K, set to 0. to use value from slow data.\n",
+ "gain = 1 # the detector's gain setting, It is later read from file and this value is overwritten\n",
+ "bias_voltage = 0. # the detector's bias voltage. set to 0. to use value from slow data.\n",
"integration_time = 70\n",
"photon_energy = 1.6 # Al fluorescence in keV\n",
"\n",
@@ -134,56 +137,6 @@
" sequences = None"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Each xcal.HistogramCalculator requires a total number of bins and a binning range. We define these using a \n",
- "# dictionary:\n",
- "\n",
- "# For all xcal histograms:\n",
- "if gain == 1:\n",
- " Hist_Bin_Dict = {\n",
- " \"bins\": 70000, # number of bins \n",
- " \"bin_range\": [0, 70000]\n",
- " }\n",
- "\n",
- " # For the numpy histograms on the last cell of the notebook:\n",
- " Event_Bin_Dict = {\n",
- " \"event_bins\": 1000, # number of bins \n",
- " \"b_range\": [0, 50000] # bin range \n",
- " }\n",
- " \n",
- "elif gain == 64:\n",
- " # For all xcal histograms:\n",
- " Hist_Bin_Dict = {\n",
- " \"bins\": 25000, # number of bins \n",
- " \"bin_range\": [0, 25000] \n",
- " }\n",
- " # For the numpy histograms on the last cell of the notebook:\n",
- " Event_Bin_Dict = {\n",
- " \"event_bins\": 1000, # number of bins \n",
- " \"b_range\": [0, 3000] # bin range \n",
- " }\n",
- " \n",
- "bins = Hist_Bin_Dict[\"bins\"]\n",
- "bin_range = Hist_Bin_Dict[\"bin_range\"]\n",
- "event_bins = Event_Bin_Dict[\"event_bins\"]\n",
- "b_range = Event_Bin_Dict[\"b_range\"]\n",
- "\n",
- "# On the singles spectrum (uploaded in the middle of this notebook), the ADU values correspoding to the boundaries\n",
- "# of the first peak region are used as cti_limit_low and cti_limit_high:\n",
- "\n",
- "if gain == 1:\n",
- " cti_limit_low = 3000 # lower limit of cti\n",
- " cti_limit_high = 10000 # higher limit of cti\n",
- "elif gain == 64:\n",
- " cti_limit_low = 50\n",
- " cti_limit_high = 170"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
@@ -224,9 +177,15 @@
" creation_time = get_dir_creation_date(in_folder, run)\n",
"\n",
"\n",
- "print(f\"Creation time: {creation_time}\")\n",
- " \n",
- "\n",
+ "print(f\"Creation time: {creation_time}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
"# Reading all sequences of the run:\n",
"file_list = []\n",
"total_sequences = 0\n",
@@ -249,6 +208,42 @@
"print(f\"This run has a total number of {total_sequences} sequences.\\n\")"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# extract slow data\n",
+ "if karabo_da_control:\n",
+ "\n",
+ " ctrl_fname = os.path.join(ped_dir, path_template.format(run, karabo_da_control)).format(sequence)\n",
+ " ctrl_path = h5path_ctrl.format(karabo_id)\n",
+ " mdl_ctrl_path = \"/CONTROL/{}/MDL/\".format(karabo_id)\n",
+ "\n",
+ " with h5py.File(ctrl_fname, \"r\") as f:\n",
+ " if bias_voltage == 0 :\n",
+ " bias_voltage = abs(f[os.path.join(mdl_ctrl_path, \"DAQ_MPOD/u0voltage/value\")][0])\n",
+ " gain = f[os.path.join(mdl_ctrl_path, \"DAQ_GAIN/spNCCDGain/value\")][0]\n",
+ " if fix_temperature == 0.:\n",
+ " fix_temperature = f[os.path.join(ctrl_path, \"inputA/krdg/value\")][0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Printing the Parameters Read from the Data File:\n",
+ "\n",
+ "display(Markdown('### Detector Parameters'))\n",
+ "print(\"Bias voltage is {} V.\".format(bias_voltage))\n",
+ "print(\"Detector gain is set to {}.\".format(gain))\n",
+ "print(\"Detector integration time is set to {} ms\".format(integration_time))\n",
+ "print(f\"Using a fixed temperature of {temperature_k} K\")"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -297,6 +292,58 @@
" raise AttributeError(\"Output path exists! Exiting\") "
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Each xcal.HistogramCalculator requires a total number of bins and a binning range. We define these using a \n",
+ "# dictionary:\n",
+ "\n",
+ "# For all xcal histograms:\n",
+ "if gain == 1:\n",
+ " Hist_Bin_Dict = {\n",
+ " \"bins\": 70000, # number of bins \n",
+ " \"bin_range\": [0, 70000]\n",
+ " }\n",
+ "\n",
+ " # For the numpy histograms on the last cell of the notebook:\n",
+ " Event_Bin_Dict = {\n",
+ " \"event_bins\": 1000, # number of bins \n",
+ " \"b_range\": [0, 50000] # bin range \n",
+ " }\n",
+ " \n",
+ "#TODO: make it more adaptive for more than only 2 gains [below was for gain==64 only]\n",
+ "else:\n",
+ " # For all xcal histograms:\n",
+ " Hist_Bin_Dict = {\n",
+ " \"bins\": 25000, # number of bins \n",
+ " \"bin_range\": [0, 25000] \n",
+ " }\n",
+ " # For the numpy histograms on the last cell of the notebook:\n",
+ " Event_Bin_Dict = {\n",
+ " \"event_bins\": 1000, # number of bins \n",
+ " \"b_range\": [0, 3000] # bin range \n",
+ " }\n",
+ " \n",
+ "bins = Hist_Bin_Dict[\"bins\"]\n",
+ "bin_range = Hist_Bin_Dict[\"bin_range\"]\n",
+ "event_bins = Event_Bin_Dict[\"event_bins\"]\n",
+ "b_range = Event_Bin_Dict[\"b_range\"]\n",
+ "\n",
+ "# On the singles spectrum (uploaded in the middle of this notebook), the ADU values correspoding to the boundaries\n",
+ "# of the first peak region are used as cti_limit_low and cti_limit_high:\n",
+ "\n",
+ "if gain == 1:\n",
+ " cti_limit_low = 3000 # lower limit of cti\n",
+ " cti_limit_high = 10000 # higher limit of cti\n",
+ "#TODO: make it more adaptive for more than only 2 gains [below was for gain==64 only\n",
+ "else:\n",
+ " cti_limit_low = 50\n",
+ " cti_limit_high = 170"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -310,7 +357,7 @@
"metadata": {},
"outputs": [],
"source": [
- "def get_dark(db_parms, bias_voltage, integration_time, gain, fix_temperature, creation_time, run):\n",
+ "def get_dark(db_parms, bias_voltage, integration_time, gain, temperature_k, creation_time, run):\n",
"# This function is to retrieve the dark constants associated with the run of interest:\n",
"\n",
" cal_db_interface, cal_db_timeout = db_parms\n",
@@ -331,13 +378,13 @@
" condition = Conditions.Dark.CCD(bias_voltage=bias_voltage,\n",
" integration_time=integration_time,\n",
" gain_setting=gain,\n",
- " temperature=fix_temperature,\n",
+ " temperature=temperature_k,\n",
" pixels_x=pixels_x,\n",
" pixels_y=pixels_y)\n",
" \n",
" for const in constants.keys():\n",
" constants[const], when[const] = \\\n",
- " get_constant_from_db_and_time(Detectors.PnCCD1,\n",
+ " get_constant_from_db_and_time(getattr(Detectors, db_module),\n",
" getattr(Constants.CCD(DetectorTypes.pnCCD), const)(),\n",
" condition,\n",
" np.zeros((pixels_x, pixels_y, 1)),\n",
@@ -367,7 +414,7 @@
"\n",
"db_parms = cal_db_interface, cal_db_timeout\n",
"\n",
- "constants = get_dark(db_parms, bias_voltage, integration_time, gain, fix_temperature, creation_time, run)\n",
+ "constants = get_dark(db_parms, bias_voltage, integration_time, gain, temperature_k, creation_time, run)\n",
"\n",
"fig = xana.heatmapPlot(constants[\"Offset\"][:,:,0], x_label='Columns', y_label='Rows', lut_label='Pedestal (ADU)', aspect=1, \n",
" x_range=(0, pixels_y), y_range=(0, pixels_x), vmax=16000, \n",
@@ -402,13 +449,13 @@
" condition = Conditions.Illuminated.CCD(bias_voltage=bias_voltage,\n",
" integration_time=integration_time,\n",
" gain_setting=gain,\n",
- " temperature=fix_temperature,\n",
+ " temperature=temperature_k,\n",
" pixels_x=pixels_x,\n",
" pixels_y=pixels_y, \n",
" photon_energy=photon_energy)\n",
"\n",
" constants[\"RelativeGain\"], relgain_time = \\\n",
- " get_constant_from_db_and_time(Detectors.PnCCD1,\n",
+ " get_constant_from_db_and_time(getattr(Detectors, db_module),\n",
" Constants.CCD(DetectorTypes.pnCCD).RelativeGain(),\n",
" condition,\n",
" np.zeros((pixels_x, pixels_y)),\n",
@@ -806,7 +853,8 @@
"\n",
"if gain == 1:\n",
" x_range = (0, 30000)\n",
- "elif gain == 64:\n",
+ "#TODO: make it more adaptive for more than only 2 gains [below was for gain==64 only\n",
+ "else:\n",
" x_range = (0, 1000)"
]
},
--
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