diff --git a/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb b/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
index aed7d9f9684891c1aff3b330e84b5ea124897003..ba512df7877a8868ae79454b68e80a610bb9ca1c 100644
--- a/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
+++ b/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
@@ -39,7 +39,7 @@
"\n",
"# Parameters affecting corrected data.\n",
"relative_gain = True # do relative gain correction.\n",
- "strixel_sensor = False # reordering for strixel detector layout.\n",
+ "strixel_sensor = \"\" # reordering for strixel detector layout. Possible strixels to choose from are A0123 and A1256.\n",
"strixel_double_norm = 2.0 # normalization to use for double-size pixels, only applied for strixel sensors.\n",
"limit_trains = 0 # ONLY FOR TESTING. process only first N trains, Use 0 to process all.\n",
"chunks_ids = 32 # HDF chunk size for memoryCell and frameNumber.\n",
@@ -95,7 +95,7 @@
"from cal_tools.calcat_interface import JUNGFRAU_CalibrationData\n",
"from cal_tools.enums import BadPixels\n",
"from cal_tools.files import DataFile\n",
- "from cal_tools.jungfraulib import JungfrauCtrl\n",
+ "from cal_tools.jungfrau.jungfraulib import JungfrauCtrl\n",
"from cal_tools.plotting import init_jungfrau_geom\n",
"from cal_tools.step_timing import StepTimer\n",
"from cal_tools.tools import (\n",
@@ -382,8 +382,13 @@
"outputs": [],
"source": [
"if strixel_sensor:\n",
- " from cal_tools.jfstrixel import STRIXEL_SHAPE as strixel_frame_shape, double_pixel_indices, to_strixel\n",
- " Ydouble, Xdouble = double_pixel_indices()\n",
+ " from cal_tools.jungfrau.jfstrixel import get_strixel_parameters, to_strixel\n",
+ " strx_params = get_strixel_parameters(strixel_sensor)\n",
+ "\n",
+ " strixel_shape = strx_params[\"frame_shape\"]\n",
+ " Ydouble = strx_params.get(\"ydouble\", slice(None))\n",
+ " Xdouble = strx_params.get(\"xdouble\", slice(None))\n",
+ "\n",
" print('Strixel sensor transformation enabled')"
]
},
@@ -400,7 +405,7 @@
" \n",
" # Copy gain over first to keep it at the original 3 for low gain.\n",
" if strixel_sensor:\n",
- " to_strixel(g, out=gain_corr[index, ...])\n",
+ " to_strixel(g, out=gain_corr[index, ...], kind=strixel_sensor)\n",
" else:\n",
" gain_corr[index, ...] = g\n",
"\n",
@@ -457,9 +462,9 @@
" msk = np.choose(g, np.moveaxis(mask_cell, -1, 0))\n",
"\n",
" if strixel_sensor:\n",
- " to_strixel(d, out=data_corr[index, ...])\n",
+ " to_strixel(d, out=data_corr[index, ...], kind=strixel_sensor)\n",
" data_corr[index, :, Ydouble, Xdouble] /= strixel_double_norm\n",
- " to_strixel(msk, out=mask_corr[index, ...])\n",
+ " to_strixel(msk, out=mask_corr[index, ...], kind=strixel_sensor)\n",
" else:\n",
" data_corr[index, ...] = d\n",
" mask_corr[index, ...] = msk"
@@ -583,7 +588,7 @@
" \n",
" # Determine total output shape.\n",
" if strixel_sensor:\n",
- " oshape = (*ishape[:-2], *strixel_frame_shape)\n",
+ " oshape = (*ishape[:-2], *strixel_shape)\n",
" else:\n",
" oshape = ishape\n",
"\n",
@@ -812,6 +817,12 @@
" vmin=_corrected_vmin, vmax=_corrected_vmax, cmap=\"jet\"\n",
")\n",
"\n",
+ "if strixel_sensor:\n",
+ " if strixel_sensor == \"A1256\":\n",
+ " aspect = 1/3\n",
+ " else: # A0123\n",
+ " aspect = 10\n",
+ "\n",
"if not strixel_sensor:\n",
" geom.plot_data_fast(\n",
" corrected_mean,\n",
@@ -820,8 +831,9 @@
" **mean_plot_kwargs\n",
" )\n",
"else:\n",
- " ax.imshow(corrected_mean.squeeze(), aspect=10, **mean_plot_kwargs)\n",
- " \n",
+ " corr = ax.imshow(corrected_mean.squeeze(), aspect=aspect, **mean_plot_kwargs)\n",
+ " plt.colorbar(corr)\n",
+ "\n",
"ax.set_title(f'{karabo_id} - Mean CORRECTED', size=18)\n",
"\n",
"plt.show()"
@@ -847,7 +859,8 @@
" **mean_plot_kwargs\n",
" )\n",
"else:\n",
- " ax.imshow(corrected_mean.squeeze(), aspect=10, **mean_plot_kwargs)\n",
+ " corr = ax.imshow(corrected_masked_mean.squeeze(), aspect=aspect, **mean_plot_kwargs)\n",
+ " plt.colorbar(corr)\n",
"\n",
"ax.set_title(f'{karabo_id} - Mean CORRECTED with mask', size=18)\n",
"\n",
@@ -878,7 +891,8 @@
" **single_plot_kwargs\n",
" )\n",
"else:\n",
- " ax.imshow(corrected_train.squeeze(), aspect=10, **single_plot_kwargs)\n",
+ " corr = ax.imshow(corrected_train.squeeze(), aspect=aspect, **single_plot_kwargs)\n",
+ " plt.colorbar(corr)\n",
"\n",
"ax.set_title(f\"{karabo_id} - CORRECTED train: {tid}\", size=18)\n",
"\n",
@@ -1048,7 +1062,9 @@
" colorbar={'shrink': 1, 'pad': 0.01},\n",
" )\n",
"else:\n",
- " ax.imshow(np.log2(mask_train).squeeze(), vmin=0, vmax=32, cmap='jet', aspect=10)\n",
+ " mask = ax.imshow(\n",
+ " mask_train.squeeze(), vmin=0, vmax=32, cmap='jet', aspect=aspect)\n",
+ " plt.colorbar(mask)\n",
"\n",
"plt.show()"
]
diff --git a/notebooks/Jungfrau/Jungfrau_dark_analysis_all_gains_burst_mode_NBC.ipynb b/notebooks/Jungfrau/Jungfrau_dark_analysis_all_gains_burst_mode_NBC.ipynb
index 5a8fc2d9917208951302c01a412d9ef24a84769a..e30b4be5656909ed64f1f102242fa7bc361f8907 100644
--- a/notebooks/Jungfrau/Jungfrau_dark_analysis_all_gains_burst_mode_NBC.ipynb
+++ b/notebooks/Jungfrau/Jungfrau_dark_analysis_all_gains_burst_mode_NBC.ipynb
@@ -90,7 +90,8 @@
"\n",
"from XFELDetAna.plotting.heatmap import heatmapPlot\n",
"from XFELDetAna.plotting.histogram import histPlot\n",
- "from cal_tools import jungfraulib, step_timing\n",
+ "from cal_tools import step_timing\n",
+ "from cal_tools.jungfrau import jungfraulib\n",
"from cal_tools.enums import BadPixels, JungfrauGainMode\n",
"from cal_tools.tools import (\n",
" get_dir_creation_date,\n",
diff --git a/src/cal_tools/jfstrixel.py b/src/cal_tools/jfstrixel.py
deleted file mode 100644
index 189b036f6cddf4aaa8ba802e908a999afb6cdfe3..0000000000000000000000000000000000000000
--- a/src/cal_tools/jfstrixel.py
+++ /dev/null
@@ -1,161 +0,0 @@
-
-import numpy as np
-
-
-REGULAR_SHAPE = (512, 1024)
-STRIXEL_SHAPE = (86, 3090)
-
-
-def _normal_indices():
- """Build normal size pixel indices."""
-
- # Normal pixels
- yin = np.arange(256)
- xin = np.arange(1024)
-
- Yin, Xin = np.meshgrid(yin, xin)
- Yout, Xout = np.meshgrid(yin // 3, (xin // 256 * 774) + (xin % 256) * 3)
- Xout += (yin % 3).astype(int)[None, :]
-
- return Yout, Xout, Yin, Xin
-
-
-def _gap_indices(in_gap_offset=0, out_gap_offset=0,
- xout_factor=+1, yout_offset=0):
- """Build one half of double size gap pixel indices."""
-
- igap = np.arange(3)
- yin = np.arange(256)
-
- Yin, Xin = np.meshgrid(yin, igap * 256 + 255 + in_gap_offset)
- Yout, Xout = np.meshgrid(yin // 6 * 2, igap * 774 + 765 + out_gap_offset)
- Xout += xout_factor * (yin % 6).astype(int)[None, :]
- Yout += yout_offset
-
- return Yout, Xout, Yin, Xin
-
-
-def transformation_indices2d():
- """Build 2D strixel transformation index arrays."""
-
- # Each of this index sets contains four 2D index arrays
- # Yout, Xout, Yin, Xin from different parts constituting the full
- # strixel frame. They are each concatenated across these parts into
- # four final index arrays to be used for translating between the
- # regular frame and the strixel frame.
- index_sets = [
- _normal_indices(),
-
- # Left gap
- _gap_indices(0, 0, +1, 0), _gap_indices(0, 0, +1, 1),
-
- # Right gap
- _gap_indices(1, 11, -1, 0), _gap_indices(1, 11, -1, 1)
- ]
-
- # Yout, Xout, Yin, Xin
- # Casting to int64 improves indexing performance by up to 30%.
- return [np.concatenate(index_set).astype(np.int64)
- for index_set in zip(*index_sets)]
-
-
-def transformation_indices1d():
- """Build 1D strixel transformation index arrays.
-
- Internally this function reduces the 2D index arrays to a single
- dimension to operate on raveled data arrays. This improves the
- transformation performance substantially by up to 3x.
- """
-
- Yout, Xout, Yin, Xin = transformation_indices2d()
-
- regular_pixel_idx = np.arange(np.prod(REGULAR_SHAPE), dtype=np.uint32) \
- .reshape(REGULAR_SHAPE)
- strixel_pixel_idx = np.empty(STRIXEL_SHAPE, dtype=np.int64)
- strixel_pixel_idx.fill(-1)
- strixel_pixel_idx[Yout, Xout] = regular_pixel_idx[Yin, Xin]
-
- Iout = np.where(strixel_pixel_idx.ravel() != -1)[0].astype(np.int64)
- Iin = strixel_pixel_idx.ravel()[Iout].astype(np.int64)
-
- return Iout, Iin
-
-
-def double_pixel_indices():
- """Build index arrays for double-size pixels.
-
- In raw data, the entire columns 255, 256, 511, 512, 767 and 768
- are double-size pixels. After strixelation, these end up in columns
- 765-776, 1539-1550 and 2313-2324 on rows 0-85 or 0-83, with a set
- of four columns with 86 rows followed by a set of 84 and 86 again.
-
- This function builds the index arrays for double pixels after
- strixelation.
-
- Returns:
- (ndarray, ndarray) 2D index arrays for double pixel Y and X.
- """
-
- Ydouble = []
- Xdouble = []
-
- for double_col in [765, 1539, 2313]:
- for col in range(double_col, double_col+12):
- for row in range(84 if ((col-double_col) // 4) == 1 else 86):
- Ydouble.append(row)
- Xdouble.append(col)
-
- return np.array(Ydouble), np.array(Xdouble)
-
-
-def to_strixel(data, out=None):
- """Transform from regular to strixel geometry.
-
- Only the last two axes are considered for transformation, input data
- may have any number of additional axes in front.
-
- Args:
- data (array_like): Data in regular geometry.
- out (array_like, optional): Buffer for transformed output, a new
- one is allocated if omitted. Must match all non-frame axes
- of input data and able to hold strixel frame.
-
- Returns:
- (array_like) Data in strixel geometry.
- """
-
- if out is None:
- out = np.zeros((*data.shape[:-2], *STRIXEL_SHAPE), dtype=data.dtype)
-
- out.reshape(*out.shape[:-2], -1)[..., Iout] = data.reshape(
- *data.shape[:-2], -1)[..., Iin]
-
- return out
-
-
-def from_strixel(data, out=None):
- """Transform from strixel to regular geometry.
-
- Only the last two axes are considered for transformation, input data
- may have any number of additional axes in front.
-
- Args:
- data (array_like): Data in strixel geometry.
- out (array_like, optional): Buffer for transformed output, a new
- one is allocated if omitted. Must match all non-frame axes
- of input data and able to hold regular frame.
-
- Returns:
- (array_like): Data in regular geometry.
- """
-
- if out is None:
- out = np.zeros((*data.shape[:-2], *REGULAR_SHAPE), dtype=data.dtype)
-
- out.reshape(*out.shape[:-2], -1)[..., Iin] = data.reshape(
- *data.shape[:-2], -1)[..., Iout]
-
- return out
-
-
-Iout, Iin = transformation_indices1d()
diff --git a/src/cal_tools/jungfrau/__init__.py b/src/cal_tools/jungfrau/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/cal_tools/jungfrau/jfstrixel.py b/src/cal_tools/jungfrau/jfstrixel.py
new file mode 100644
index 0000000000000000000000000000000000000000..b388e5063430295174da65bbdee4a53476073796
--- /dev/null
+++ b/src/cal_tools/jungfrau/jfstrixel.py
@@ -0,0 +1,120 @@
+
+from functools import lru_cache
+from pathlib import Path
+
+import numpy as np
+
+REGULAR_SHAPE = (512, 1024)
+DIR_PATH = package_directory = Path(__file__).resolve().parent
+
+
+@lru_cache
+def get_strixel_parameters(kind):
+ """Returns a dictionary of strixel parameters stored in .npz file
+ based on the given kind.
+
+ Args:
+ kind (str): Specifies the type of strixel parameters to retrieve.
+ There is two possible values: "A0123" or "A1256"
+ Returns:
+ (dict): Dictionary contating the strixel parameters.
+ """
+ strx_parameters = {}
+
+ if kind == "A0123":
+ file_path = DIR_PATH / "strixel_cols_A0123-lut_mask.npz"
+ elif kind == "A1256":
+ file_path = DIR_PATH / "strixel_rows_A1256-lut_mask.npz"
+
+ with np.load(file_path) as data:
+ for k in data.files:
+ strx_parameters[k] = data[k]
+
+ return strx_parameters
+
+
+def store_double_pixel_indices():
+ """Build index arrays for double-size pixels.
+
+ In raw data for A0123 strixel detector,
+ the entire columns 255, 256, 511, 512, 767 and 768
+ are double-size pixels. After strixelation, these end up in columns
+ 765-776, 1539-1550 and 2313-2324 on rows 0-85 or 0-83, with a set
+ of four columns with 86 rows followed by a set of 84 and 86 again.
+
+ This function builds the index arrays for double pixels after
+ strixelation and stores it in the available A0123 .npz file.
+ """
+
+ ydouble = []
+ xdouble = []
+ file_path = DIR_PATH / "strixel_cols_A0123-lut_mask.npz"
+
+ with np.load(file_path) as data:
+ for double_col in [765, 1539, 2313]:
+ for col in range(double_col, double_col+12):
+ for row in range(84 if ((col-double_col) // 4) == 1 else 86):
+ ydouble.append(row)
+ xdouble.append(col)
+ np.savez(file_path, **data, ydouble=ydouble, xdouble=xdouble)
+
+
+def to_strixel(data, out=None, kind="A0123"):
+ """Transform from regular to strixel geometry.
+
+ Only the last two axes are considered for transformation, input data
+ may have any number of additional axes in front.
+
+ Args:
+ data (array_like): Data in regular geometry.
+ out (array_like, optional): Buffer for transformed output, a new
+ one is allocated if omitted. Must match all non-frame axes
+ of input data and able to hold strixel frame.
+
+ Returns:
+ (array_like) Data in strixel geometry.
+ """
+
+ if kind is None:
+ return data
+
+ strx = get_strixel_parameters(kind)
+
+ if out is None:
+ out = np.zeros(
+ (*data.shape[:-2], *strx["frame_shape"]), dtype=data.dtype)
+
+ out.reshape(*out.shape[:-2], -1)[..., ~strx["mask"]] = data.reshape(
+ *data.shape[:-2], -1)[..., strx["lut"]]
+
+ return out
+
+
+def from_strixel(data, out=None, kind="A0123"):
+ """Transform from strixel to regular geometry.
+
+ Only the last two axes are considered for transformation, input data
+ may have any number of additional axes in front.
+
+ Args:
+ data (array_like): Data in strixel geometry.
+ out (array_like, optional): Buffer for transformed output, a new
+ one is allocated if omitted. Must match all non-frame axes
+ of input data and able to hold regular frame.
+
+ Returns:
+ (array_like): Data in regular geometry.
+ """
+
+ if kind is None:
+ return data
+
+ strx = get_strixel_parameters(kind)
+
+ if out is None:
+ out = np.zeros((*data.shape[:-2], *REGULAR_SHAPE), dtype=data.dtype)
+
+ out.reshape(*out.shape[:-2], -1)[..., strx["lut"]] = data.reshape(
+ *data.shape[:-2], -1)[..., strx["mask"]]
+
+ return out
diff --git a/src/cal_tools/jungfraulib.py b/src/cal_tools/jungfrau/jungfraulib.py
similarity index 100%
rename from src/cal_tools/jungfraulib.py
rename to src/cal_tools/jungfrau/jungfraulib.py
diff --git a/src/cal_tools/jungfrau/strixel_cols_A0123-lut_mask.npz b/src/cal_tools/jungfrau/strixel_cols_A0123-lut_mask.npz
new file mode 100644
index 0000000000000000000000000000000000000000..fcf08e279709b7a8a64c858fdd932d3c7738c172
Binary files /dev/null and b/src/cal_tools/jungfrau/strixel_cols_A0123-lut_mask.npz differ
diff --git a/src/cal_tools/jungfrau/strixel_rows_A1256-lut_mask.npz b/src/cal_tools/jungfrau/strixel_rows_A1256-lut_mask.npz
new file mode 100644
index 0000000000000000000000000000000000000000..be31b53bb7bb0fe2cf1060468e21438793c33dd3
Binary files /dev/null and b/src/cal_tools/jungfrau/strixel_rows_A1256-lut_mask.npz differ
diff --git a/tests/test_jungfraulib.py b/tests/test_jungfraulib.py
index c7fb5258ba2c0035cbad2668e4bdc35131866855..00a3ae5b9f2e4761360733d95f62e308da08016e 100644
--- a/tests/test_jungfraulib.py
+++ b/tests/test_jungfraulib.py
@@ -1,7 +1,7 @@
import pytest
from extra_data import RunDirectory
-from cal_tools.jungfraulib import JungfrauCtrl, sort_runs_by_gain
+from cal_tools.jungfrau.jungfraulib import JungfrauCtrl, sort_runs_by_gain
# TODO: replace with mocked RAW data as in tests/test_agipdlib.py
JF = JungfrauCtrl(