diff --git a/notebooks/Gotthard2/Characterize_Darks_Gotthard2_NBC.ipynb b/notebooks/Gotthard2/Characterize_Darks_Gotthard2_NBC.ipynb
index 32a4f221cf4e6fcbb67ffdaf8f8b9877000d69b4..3ecf9f72b38e5f15c8a8e3895f62160140f9b0f4 100644
--- a/notebooks/Gotthard2/Characterize_Darks_Gotthard2_NBC.ipynb
+++ b/notebooks/Gotthard2/Characterize_Darks_Gotthard2_NBC.ipynb
@@ -51,6 +51,8 @@
"exposure_period = -1. # Detector exposure period, set to -1 to use value in raw file.\n",
"acquisition_rate = -1. # Detector acquisition rate (1.1/4.5), set to -1 to use value in raw file.\n",
"single_photon = -1 # Detector single photon mode (High/Low CDS), set to -1 to use value in raw file.\n",
+ "# This is used for the summary notebook\n",
+ "sensor_type = \"\" # Specifies the GH2 sensor type: '50um', '25um', or \"\" to automatically decide from RUN data.\n",
"\n",
"# Parameters used for calculating calibration constants\n",
"bpix_noise_nitrs = 5 # number of maximum iterations to actively try to reach correct number of badpixels of NOISE_OUT_OF_THRESHOLD type. Leave -1 to keep iterating until finding maximum number of badpixels.\n",
@@ -97,6 +99,17 @@
"%matplotlib inline"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc4a9824",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Input parameters validation\n",
+ "gotthard2lib.validate_sensor_type(sensor_type)"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -206,9 +219,9 @@
"print(\"Single photon: \", single_photon)\n",
"acquisition_rate = conditions[\"acquisition_rate\"].pop()\n",
"print(\"Acquisition rate: \", acquisition_rate)\n",
- "\n",
- "gh2_detector = g2ctrl.get_det_type()\n",
- "print(f\"Processing {gh2_detector} Gotthard2.\")\n"
+ "if not sensor_type:\n",
+ " sensor_type = g2ctrl.get_sensor_type()\n",
+ "print(f\"Processing {sensor_type} Gotthard2.\")"
]
},
{
diff --git a/notebooks/Gotthard2/Correction_Gotthard2_NBC.ipynb b/notebooks/Gotthard2/Correction_Gotthard2_NBC.ipynb
index c81046453cef6cf3afe02690b99059114407972c..b44d20e869d32179b79deb47c9d6e666ec330c7a 100644
--- a/notebooks/Gotthard2/Correction_Gotthard2_NBC.ipynb
+++ b/notebooks/Gotthard2/Correction_Gotthard2_NBC.ipynb
@@ -84,6 +84,7 @@
"acquisition_rate = -1. # Detector acquisition rate (1.1/4.5), set to -1 to use value in raw file.\n",
"single_photon = -1 # Detector single photon mode (High/Low CDS), set to -1 to use value in raw file.\n",
"reverse_second_module = -1 # Reverse 25um GH2 second module before interleaving. set to -1 to use value in raw file to reverse based on `CTRL/reverseSlaveReadOutMode`'s value.\n",
+ "sensor_type = \"\" # Specifies the GH2 sensor type: '50um', '25um', or \"\" to automatically decide from RUN data.\n",
"\n",
"# Parameters for plotting\n",
"skip_plots = False # exit after writing corrected files\n",
@@ -105,7 +106,6 @@
"import warnings\n",
"from logging import warning\n",
"\n",
- "import h5py\n",
"import pasha as psh\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
@@ -131,6 +131,17 @@
"%matplotlib inline"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc893eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Input parameters validation\n",
+ "gotthard2lib.validate_sensor_type(sensor_type)"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -188,9 +199,17 @@
" acquisition_rate = g2ctrl.get_acquisition_rate()\n",
"if single_photon == -1:\n",
" single_photon = g2ctrl.get_single_photon()\n",
+ "if not sensor_type:\n",
+ " sensor_type = g2ctrl.get_sensor_type()\n",
+ "\n",
+ "# Check if the GH2 detector type is \"50um\" and validate that only one data aggregator is provided\n",
+ "if sensor_type == \"50um\" and len(karabo_da) > 1:\n",
+ " raise ValueError(\n",
+ " f\"karabo_da has more than one aggregator {karabo_da}, \"\n",
+ " \"but the GH2 detector type is specified as '50um', which expects only one aggregator.\"\n",
+ " )\n",
"\n",
- "gh2_detector = g2ctrl.get_det_type()\n",
- "if reverse_second_module == -1 and gh2_detector == \"25um\":\n",
+ "if reverse_second_module == -1 and sensor_type == \"25um\":\n",
" reverse_second_module = not g2ctrl.second_module_reversed()\n",
" if reverse_second_module:\n",
" warning(\n",
@@ -202,7 +221,7 @@
"print(\"Exposure Period:\", exposure_period)\n",
"print(\"Acquisition Rate:\", acquisition_rate)\n",
"print(\"Single Photon:\", single_photon)\n",
- "print(f\"Processing {gh2_detector} Gotthard2.\")"
+ "print(f\"Processing {sensor_type} Gotthard2.\")"
]
},
{
@@ -435,7 +454,7 @@
" print(f\"Correcting {n_trains} for {raw_file}.\")\n",
"\n",
" # Initialize GH2 data and gain arrays to store in corrected files.\n",
- " if gh2_detector == \"25um\":\n",
+ " if sensor_type == \"25um\":\n",
" dshape_stored = (dc[data_sources[0], \"data.adc\"].shape[:2] + (1280 * 2,))\n",
" data_stored = np.zeros(dshape_stored, dtype=np.float32)\n",
" gain_stored = np.zeros(dshape_stored, dtype=np.uint8)\n",
@@ -477,7 +496,7 @@
" # Create CORR files and add corrected data sections.\n",
" image_counts = dc[src, \"data.adc\"].data_counts(labelled=False)\n",
"\n",
- " if gh2_detector == \"25um\":\n",
+ " if sensor_type == \"25um\":\n",
" data_stored[..., i::2] = data_corr\n",
" gain_stored[..., i::2] = gain\n",
" mask_stored[..., i::2] = mask\n",
@@ -568,14 +587,14 @@
" )\n",
" mask = out_dc[corr_data_source, \"data.mask\"].train_from_id(tid)[1]\n",
"\n",
- "if gh2_detector == \"25um\":\n",
+ "if sensor_type == \"25um\":\n",
" mod_dcs[corr_data_source][\"train_raw_data\"] = np.zeros((data_corr.shape[1], 1280 * 2), dtype=np.float32)\n",
" mod_dcs[corr_data_source][\"train_raw_gain\"] = np.zeros((data_corr.shape[1], 1280 * 2), dtype=np.uint8)\n",
"\n",
"for i, src in enumerate(data_sources):\n",
" with H5File(first_seq_raw) as in_dc:\n",
" train_dict = in_dc.train_from_id(tid)[1][src]\n",
- " if gh2_detector == \"25um\":\n",
+ " if sensor_type == \"25um\":\n",
" if reverse_second_module and i == 1:\n",
" data = np.flip(train_dict[\"data.adc\"], axis=-1)\n",
" gain = np.flip(train_dict[\"data.gain\"], axis=-1)\n",
@@ -612,8 +631,8 @@
"display(Markdown(\"### Mean RAW and CORRECTED across pulses for one train:\"))\n",
"display(Markdown(f\"Train: {tid}\"))\n",
"\n",
- "# Set title format based on gh2_detector\n",
- "if gh2_detector == \"50um\":\n",
+ "# Set title format based on sensor_type\n",
+ "if sensor_type == \"50um\":\n",
" title_format = f\"{{}} data for {karabo_da} ({db_modules})\"\n",
"else:\n",
" title_format = f\"Interleaved {{}} data for {karabo_da} ({db_modules})\"\n",
diff --git a/notebooks/Gotthard2/Summary_Darks_Gotthard2_NBC.ipynb b/notebooks/Gotthard2/Summary_Darks_Gotthard2_NBC.ipynb
index 3780f9266e4eb90cd65fc85e31116f3aea8fd465..adda2211f2a982e967940dc40818b5eb480cd6cd 100644
--- a/notebooks/Gotthard2/Summary_Darks_Gotthard2_NBC.ipynb
+++ b/notebooks/Gotthard2/Summary_Darks_Gotthard2_NBC.ipynb
@@ -31,6 +31,8 @@
"ctrl_source_template = '{karabo_id}/DET/{control_template}' # template for control source name (filled with karabo_id_control)\n",
"karabo_id_control = \"\" # Control karabo ID. Set to empty string to use the karabo-id\n",
"\n",
+ "sensor_type = \"\" # Specifies the GH2 sensor type: '50um', '25um', or \"\" to automatically decide from RUN data.\n",
+ "\n",
"# Parameters to be used for injecting dark calibration constants.\n",
"local_output = True # Boolean indicating that local constants were stored in the out_folder\n",
"\n",
@@ -67,6 +69,16 @@
"from cal_tools.tools import CalibrationMetadata"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Input parameters validation\n",
+ "gotthard2lib.validate_sensor_type(sensor_type)"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -91,8 +103,9 @@
" karabo_id_control=karabo_id_control,\n",
" control_template=control_template\n",
" ))\n",
- "gh2_detector = g2ctrl.get_det_type()\n",
- "print(f\"Processing {gh2_detector} Gotthard2.\")"
+ "if not sensor_type:\n",
+ " sensor_type = g2ctrl.get_sensor_type()\n",
+ "print(f\"Processing {sensor_type} Gotthard2.\")"
]
},
{
@@ -118,7 +131,7 @@
"source": [
"plt_map = dict()\n",
"dark_constants = [\"Offset\", \"Noise\", \"BadPixelsDark\"]\n",
- "if gh2_detector == \"25um\":\n",
+ "if sensor_type == \"25um\":\n",
" for cname in dark_constants:\n",
" plt_map[cname] = np.zeros(\n",
" (1280 * 2, 2, 3),\n",
@@ -138,7 +151,7 @@
" module = mod.replace(\"-\", \"/\")\n",
"\n",
" with h5py.File(out_folder / f\"const_{cname}_{pdu}.h5\", 'r') as f:\n",
- " if gh2_detector == \"25um\":\n",
+ " if sensor_type == \"25um\":\n",
" plt_map[cname][i::2] = f[\"data\"][()]\n",
" else:\n",
" plt_map[cname] = f[\"data\"][()]"
@@ -150,7 +163,7 @@
"metadata": {},
"outputs": [],
"source": [
- "if gh2_detector == \"50um\":\n",
+ "if sensor_type == \"50um\":\n",
" title = (\n",
" f\"{{}} data for \"\n",
" f\"{[f'{mod}({pdu})' for mod, pdu in mod_mapping.items()]}\"\n",
diff --git a/src/cal_tools/gotthard2/gotthard2lib.py b/src/cal_tools/gotthard2/gotthard2lib.py
index e6a8267c86de2b567a1e613120f6cde3f16fd226..faacc8add6096bc8b733b3c3d6aac80fcf98f60e 100644
--- a/src/cal_tools/gotthard2/gotthard2lib.py
+++ b/src/cal_tools/gotthard2/gotthard2lib.py
@@ -3,6 +3,16 @@ from pathlib import Path
from typing import List, Union
import extra_data
+import numpy as np
+
+
+def validate_sensor_type(sensor_type):
+ choices = ["25um", "50um"]
+ if sensor_type and sensor_type not in choices:
+ raise ValueError(
+ f"Unexpected Gotthard2 `sensor_type` given: '{sensor_type}'. "
+ f"Valid options are: {', '.join(choices)}."
+ )
def map_da_to_source(dc, das, source_template, karabo_id, affixes):
@@ -63,16 +73,29 @@ class Gotthard2Ctrl():
return bool(
self.run_dc[self.ctrl_src, "singlePhoton"].as_single_value())
- def get_det_type(self):
+ def get_sensor_type(self):
"""GH2 rxHostname is a vector of bytes objects.
GH2 25um has two host names unlike 50um which has one.
Returns:
str: return if its a 25um or 50um detector.
"""
-
- hostname = self.run_dc.get_run_value(self.ctrl_src, "rxHostname")
- return "25um" if hostname[1].decode("utf-8") else "50um"
+ hostnames = self.run_dc.get_run_value(self.ctrl_src, "rxHostname")
+ num_hosts = np.count_nonzero(hostnames != b'')
+ if num_hosts == 0:
+ raise ValueError(
+ "No hosts are present in RUN/rxHostname. "
+ "Unable to determine the sensor type for this data. ")
+
+ if num_hosts == 1:
+ return "50um"
+ elif num_hosts == 2:
+ return "25um"
+ else:
+ raise ValueError(
+ f"RUN/rxHostname has more host names ({num_hosts}) than expected. "
+ "Expected either 1 hostname for 50um or 2 host names for 25um."
+ )
def second_module_reversed(self):
"""Check if reverseSlaveReadOutMode is True."""