diff --git a/src/calng/base_condition.py b/src/calng/base_condition.py
index 96614475fe157e8b742c92cdca4043f1ea54dd53..673fa1156f93cf33a384f1c3c6b7728bdb80fe8e 100644
--- a/src/calng/base_condition.py
+++ b/src/calng/base_condition.py
@@ -12,6 +12,7 @@ from karabo.middlelayer import (
Proxy,
Slot,
State,
+ StateSignifier,
String,
VectorHash,
VectorString,
@@ -66,6 +67,7 @@ class ConditionBase(Device):
raise NotImplementedError("Subclass must implement!")
async def onInitialization(self):
+ self._state_signifier = StateSignifier()
self.state = State.INIT
self._manager_dev = await connectDevice(self.managerDeviceId.value)
if self._manager_dev.classId.value == "CalibrationManager":
@@ -149,7 +151,9 @@ class ConditionBase(Device):
self._monitor = background(aux)
self._control_devs = control_devs
- self.state = State.MONITORING
+ self.state = self._state_signifier.returnMostSignificant(
+ [State.MONITORING, State[self.conditionsMatch]]
+ )
@Slot(
allowedStates=[State.MONITORING],
@@ -296,7 +300,9 @@ class ConditionBase(Device):
async def checkConditions(self):
self.state = State.CHANGING
await self._check_or_update(False)
- self.state = State.ON
+ self.state = self._state_signifier.returnMostSignificant(
+ [State.ON, State[self.conditionsMatch]]
+ )
@Slot(
allowedStates=[State.ON],
@@ -307,7 +313,9 @@ class ConditionBase(Device):
async def updateConditions(self):
self.state = State.CHANGING
await self._check_or_update(True)
- self.state = State.ON
+ self.state = self._state_signifier.returnMostSignificant(
+ [State.ON, State[self.conditionsMatch]]
+ )
availableScenes = VectorString(
displayedName="Available scenes",
diff --git a/src/calng/base_correction.py b/src/calng/base_correction.py
index 1efd81d58ceefc617977411aee45b0bc9534a91e..00280f708232ce34a58387c61a72497d30401bcf 100644
--- a/src/calng/base_correction.py
+++ b/src/calng/base_correction.py
@@ -135,7 +135,20 @@ class BaseCorrection(PythonDevice):
.setNewDefaultValue(State.INIT)
.commit(),
- INPUT_CHANNEL(expected).key("dataInput").commit(),
+ INPUT_CHANNEL(expected)
+ .key("dataInput")
+ .commit(),
+
+ OVERWRITE_ELEMENT(expected)
+ .key("dataInput.onSlowness")
+ .setNewTags("managed")
+ .setNewDefaultValue("queueDrop")
+ .commit(),
+
+ OVERWRITE_ELEMENT(expected)
+ .key("dataInput.maxQueueLength")
+ .setNewTags("managed")
+ .commit(),
VECTOR_STRING_ELEMENT(expected)
.key("fastSources")
diff --git a/src/calng/base_kernel_runner.py b/src/calng/base_kernel_runner.py
index 14932cae9c19639f31d796a7cd51d6c35e0b93e6..933bee7d8433d2722f848163d6855fcb1cbb3a0f 100644
--- a/src/calng/base_kernel_runner.py
+++ b/src/calng/base_kernel_runner.py
@@ -140,9 +140,11 @@ class BaseKernelRunner:
out[:] = self.reshaped_data
+kernel_dir = pathlib.Path(__file__).absolute().parent / "kernels"
+
+
def get_kernel_template(kernel_fn):
- src_dir = pathlib.Path(__file__).absolute().parent
- with (src_dir / "kernels" / kernel_fn).open("r") as fd:
+ with (kernel_dir / kernel_fn).open("r") as fd:
return jinja2.Template(fd.read())
diff --git a/src/calng/corrections/JungfrauCorrection.py b/src/calng/corrections/JungfrauCorrection.py
index 64ef130d5330a00793305a09fddde8eacf7d33c8..ab5af5f688fa74c32ab02195fffde302cf305ef9 100644
--- a/src/calng/corrections/JungfrauCorrection.py
+++ b/src/calng/corrections/JungfrauCorrection.py
@@ -9,7 +9,6 @@ from karabo.bound import (
OUTPUT_CHANNEL,
OVERWRITE_ELEMENT,
STRING_ELEMENT,
- VECTOR_STRING_ELEMENT,
Schema,
)
@@ -18,7 +17,6 @@ from .. import (
base_correction,
base_kernel_runner,
schemas,
- preview_utils,
utils,
)
from .._version import version as deviceVersion
@@ -81,6 +79,7 @@ class JungfrauBaseRunner(base_kernel_runner.BaseKernelRunner):
pixels_y,
frames,
constant_memory_cells,
+ config,
output_data_dtype=np.float32,
bad_pixel_mask_value=np.nan,
):
@@ -98,23 +97,42 @@ class JungfrauBaseRunner(base_kernel_runner.BaseKernelRunner):
self.bad_pixel_map = self._xp.empty(self.map_shape, dtype=np.uint32)
self.bad_pixel_mask_value = bad_pixel_mask_value
- # strixel support
- self._strixel_out_shape = (frames, 86, 3090)
- self._strixel_block = (1, 1, 64)
- # note: only executing kernel on lower half of y range, hence 256
- self._strixel_grid = tuple(
- utils.ceil_div(a_length, block_length)
- for (a_length, block_length) in zip(
- (frames, 256, 1024), self._strixel_block
- )
- )
+ self.output_dtype = output_data_dtype
self._processed_data_regular = self._xp.empty(
self.processed_shape, dtype=output_data_dtype
)
- self._processed_data_strixel = self._xp.empty(
- self._strixel_out_shape, dtype=output_data_dtype
- )
+ self._processed_data_strixel = None
self.flush_buffers(set(Constants))
+ self.correction_kernel_strixel = None
+ self.reconfigure(config)
+
+ def reconfigure(self, config):
+ # note: regular bad pixel masking uses device property (TODO: unify)
+ if (mask_value := config.get("badPixels.maskingValue")) is not None:
+ self._bad_pixel_mask_value = self._xp.float32(mask_value)
+ # this is a functools.partial, can just update the captured parameter
+ if self.correction_kernel_strixel is not None:
+ self.correction_kernel_strixel.keywords[
+ "missing"
+ ] = self._bad_pixel_mask_value
+
+ if (strixel_type := config.get("strixel.type")) is not None:
+ # drop the friendly parenthesized name
+ strixel_type = strixel_type.partition("(")[0]
+ strixel_package = np.load(
+ base_kernel_runner.kernel_dir
+ / f"strixel_{strixel_type}-lut_mask.npz"
+ )
+ self._strixel_out_shape = tuple(strixel_package["frame_shape"])
+ self._processed_data_strixel = None
+ # TODO: use bad pixel masking config here
+ self.correction_kernel_strixel = functools.partial(
+ utils.apply_partial_lut,
+ lut=self._xp.asarray(strixel_package["lut"]),
+ mask=self._xp.asarray(strixel_package["mask"]),
+ missing=self._bad_pixel_mask_value,
+ )
+ # note: always masking unmapped pixels (not respecting NON_STANDARD_SIZE)
def load_constant(self, constant, constant_data):
if constant_data.shape[0] == self.pixels_x:
@@ -195,7 +213,6 @@ class JungfrauGpuRunner(JungfrauBaseRunner):
)
)
self.correction_kernel = source_module.get_function("correct")
- self.strixel_transform_kernel = source_module.get_function("strixel_transform")
def load_data(self, image_data, input_gain_stage, cell_table):
self.input_data.set(image_data)
@@ -220,14 +237,21 @@ class JungfrauGpuRunner(JungfrauBaseRunner):
),
)
if flags & CorrectionFlags.STRIXEL:
- self.strixel_transform_kernel(
- self._strixel_grid,
- self._strixel_block,
- (
- self._processed_data_regular,
- self._processed_data_strixel,
- ),
- )
+ if (
+ self._processed_data_strixel is None
+ or self.frames != self._processed_data_strixel.shape[0]
+ ):
+ self._processed_data_strixel = self._xp.empty(
+ (self.frames,) + self._strixel_out_shape,
+ dtype=self.output_dtype,
+ )
+ for pixel_frame, strixel_frame in zip(
+ self._processed_data_regular, self._processed_data_strixel
+ ):
+ self.correction_kernel_strixel(
+ data=pixel_frame,
+ out=strixel_frame,
+ )
self.processed_data = self._processed_data_strixel
else:
self.processed_data = self._processed_data_regular
@@ -244,13 +268,12 @@ class JungfrauCpuRunner(JungfrauBaseRunner):
self.input_cell_table = None
# for computing previews faster
- self.thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=3)
+ self.thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=16)
from ..kernels import jungfrau_cython
self.correction_kernel_single = jungfrau_cython.correct_single
self.correction_kernel_burst = jungfrau_cython.correct_burst
- self.correction_kernel_strixel = jungfrau_cython.strixel_transform
def __del__(self):
self.thread_pool.shutdown()
@@ -286,8 +309,25 @@ class JungfrauCpuRunner(JungfrauBaseRunner):
)
if flags & CorrectionFlags.STRIXEL:
- self.correction_kernel_strixel(
- self._processed_data_regular, self._processed_data_strixel
+ if (
+ self._processed_data_strixel is None
+ or self.frames != self._processed_data_strixel.shape[0]
+ ):
+ self._processed_data_strixel = self._xp.empty(
+ (self.frames,) + self._strixel_out_shape,
+ dtype=self.output_dtype,
+ )
+ concurrent.futures.wait(
+ [
+ self.thread_pool.submit(
+ self.correction_kernel_strixel,
+ data=pixel_frame,
+ out=strixel_frame,
+ )
+ for pixel_frame, strixel_frame in zip(
+ self._processed_data_regular, self._processed_data_strixel
+ )
+ ]
)
self.processed_data = self._processed_data_strixel
else:
@@ -530,6 +570,19 @@ class JungfrauCorrection(base_correction.BaseCorrection):
.key("corrections.strixel.preview")
.setNewDefaultValue(False)
.commit(),
+
+ STRING_ELEMENT(expected)
+ .key("corrections.strixel.type")
+ .description(
+ "Which kind of strixel layout is used for this module? cols_A0123 is "
+ "the first strixel layout deployed at HED and rows_A1256 is the one "
+ "later deployed at SCS."
+ )
+ .assignmentOptional()
+ .defaultValue("cols_A0123(HED-type)")
+ .options("cols_A0123(HED-type),rows_A1256(SCS-type)")
+ .reconfigurable()
+ .commit(),
)
base_correction.add_bad_pixel_config_node(expected)
JungfrauCalcatFriend.add_schema(expected)
@@ -575,6 +628,8 @@ class JungfrauCorrection(base_correction.BaseCorrection):
def _kernel_runner_init_args(self):
return {
"bad_pixel_mask_value": self.bad_pixel_mask_value,
+ # temporary: will refactor base class to always pass config node
+ "config": self.get("corrections"),
}
@property
@@ -673,6 +728,11 @@ class JungfrauCorrection(base_correction.BaseCorrection):
constant_data &= self._override_bad_pixel_flags
self.kernel_runner.load_constant(constant, constant_data)
+ def preReconfigure(self, config):
+ super().preReconfigure(config)
+ if config.has("corrections"):
+ self.kernel_runner.reconfigure(config["corrections"])
+
def postReconfigure(self):
super().postReconfigure()
diff --git a/src/calng/kernels/jungfrau_cpu.pyx b/src/calng/kernels/jungfrau_cpu.pyx
index 9b6072d41fa3c588941e400959b38728dabd8643..2a200063879007b54f92c762378863c0d3e71c22 100644
--- a/src/calng/kernels/jungfrau_cpu.pyx
+++ b/src/calng/kernels/jungfrau_cpu.pyx
@@ -88,33 +88,3 @@ def correct_single(
if (flags & REL_GAIN):
corrected = corrected / relgain_map[0, y, x, gain]
output[0, y, x] = corrected
-
-
-def strixel_transform(
- float[:, :, ::contiguous] image_data,
- float[:, :, ::contiguous] output
-):
- cdef int yin, xin, igap, ichip, xout, yout, frame
-
- for frame in range(image_data.shape[0]):
- for yin in range(256) :
- yout = int(yin / 3)
- for xin in range(1024) :
- ichip = <int>(xin / 256)
- xout = (ichip * 774) + (xin % 256) * 3 + yin % 3
- # 774 is the chip period, 256*3+6
- output[frame, yout, xout] = image_data[frame, yin, xin]
- # now the gap pixels...
- for yin in range(256):
- yout = <int>(yin / 6) * 2
- for igap in range(3) :
- # first the left side of gap
- xin = igap * 256 + 255
- xout = igap * 774 + 765 + yin % 6
- output[frame, yout, xout] = image_data[frame, yin, xin]
- output[frame, yout+1, xout] = image_data[frame, yin, xin]
- # then the right side is mirrored
- xin = igap * 256 + 255 + 1
- xout = igap * 774 + 765 + 11 - yin % 6
- output[frame, yout, xout] = image_data[frame, yin, xin]
- output[frame, yout+1, xout] = image_data[frame, yin, xin]
diff --git a/src/calng/kernels/jungfrau_gpu.cu b/src/calng/kernels/jungfrau_gpu.cu
index fc2bc326e125ddfcbe4884174ca498ff8c7e5a56..a4f148f1c49d172b31f91a6a1a4ae4ec13c61c49 100644
--- a/src/calng/kernels/jungfrau_gpu.cu
+++ b/src/calng/kernels/jungfrau_gpu.cu
@@ -83,108 +83,4 @@ extern "C" {
output[data_index] = ({{output_data_dtype}})res;
{% endif %}
}
-
- __global__ void strixel_transform(const {{output_data_dtype}}* data, // shape: memory cell, y, x
- {{output_data_dtype}}* output) {
- const size_t Xin = {{pixels_x}};
- const size_t Yin = {{pixels_y}};
- const size_t Xout = 3090;
- const size_t Yout = 86;
- const size_t input_frames = {{frames}};
-
- const size_t current_frame = blockIdx.x * blockDim.x + threadIdx.x;
- // following naming from cython version
- const size_t yin = blockIdx.y * blockDim.y + threadIdx.y;
- size_t xin = blockIdx.z * blockDim.z + threadIdx.z;
-
- // note: hardcoded limits here as only half of y-axis is used
- if (current_frame >= input_frames || yin >= 256 || xin >= 1024) {
- return;
- }
-
- // avoid race conditions by only writing these once
- const size_t overwritten_columns[18] = {
- 765,
- 766,
- 767,
- 774,
- 775,
- 776,
- 1539,
- 1540,
- 1541,
- 1548,
- 1549,
- 1550,
- 2313,
- 2314,
- 2315,
- 2322,
- 2323,
- 2324
- };
-
- const size_t data_stride_x = 1;
- const size_t data_stride_y = Xin * data_stride_x;
- const size_t data_stride_frame = Yin * data_stride_y;
-
- const size_t output_stride_x = 1;
- const size_t output_stride_y = Xout * output_stride_x;
- const size_t output_stride_frame = Yout * output_stride_y;
-
- const size_t ichip = xin / 256;
- size_t xout = (ichip * 774) + (xin % 256) * 3 + (yin % 3);
- size_t yout = yin / 3;
- bool will_be_overwritten = false;
- size_t out_index, data_index;
- for (int i=0; i<18; ++i) {
- if (xout == overwritten_columns[i]) {
- will_be_overwritten = true;
- }
- }
- if (!will_be_overwritten) {
- out_index = current_frame * output_stride_frame +
- yout * output_stride_y +
- xout * output_stride_x;
- data_index = current_frame * data_stride_frame +
- yin * data_stride_y +
- xin * data_stride_x;
- output[out_index] = data[data_index];
- }
- if (xin < 3) {
- // reuse for the gap pixel case (see cython version)
- const size_t igap = xin;
- yout = (yin / 6) * 2;
-
- // left side
- xin = igap * 256 + 255;
- xout = igap * 774 + 765 + yin % 6;
- data_index = current_frame * data_stride_frame +
- yin * data_stride_y +
- xin * data_stride_x;
- out_index = current_frame * output_stride_frame +
- yout * output_stride_y +
- xout * output_stride_x;
- output[out_index] = data[data_index];
- out_index = current_frame * output_stride_frame +
- (yout + 1) * output_stride_y +
- xout * output_stride_x;
- output[out_index] = data[data_index];
-
- // mirror right side
- xin = igap * 256 + 255 + 1;
- xout = igap * 774 + 765 + 11 - yin % 6;
- data_index = current_frame * data_stride_frame +
- yin * data_stride_y +
- xin * data_stride_x;
- out_index = current_frame * output_stride_frame +
- yout * output_stride_y +
- xout * output_stride_x;
- output[out_index] = data[data_index];
- out_index = current_frame * output_stride_frame +
- (yout + 1) * output_stride_y +
- xout * output_stride_x;
- output[out_index] = data[data_index];
- }
- }
}
diff --git a/src/calng/kernels/strixel_cols_A0123-lut_mask.npz b/src/calng/kernels/strixel_cols_A0123-lut_mask.npz
new file mode 100644
index 0000000000000000000000000000000000000000..bdef03cb602e67db2c4732733b3053a9afa35940
Binary files /dev/null and b/src/calng/kernels/strixel_cols_A0123-lut_mask.npz differ
diff --git a/src/calng/kernels/strixel_rows_A1256-lut_mask.npz b/src/calng/kernels/strixel_rows_A1256-lut_mask.npz
new file mode 100644
index 0000000000000000000000000000000000000000..be31b53bb7bb0fe2cf1060468e21438793c33dd3
Binary files /dev/null and b/src/calng/kernels/strixel_rows_A1256-lut_mask.npz differ
diff --git a/src/calng/utils.py b/src/calng/utils.py
index c97ecb47b7ac7727574c8096090aa3bc95880c65..08815412922d1793f3587cfbe80cba860c1d895c 100644
--- a/src/calng/utils.py
+++ b/src/calng/utils.py
@@ -569,3 +569,9 @@ def cell_table_to_string(cell_table):
def grid_to_cover_shape_with_blocks(full_shape, block_shape):
return tuple(itertools.starmap(ceil_div, zip(full_shape, block_shape)))
+
+
+def apply_partial_lut(data, lut, mask, out, missing=np.nan):
+ tmp = out.ravel()
+ tmp[~mask] = data.ravel()[lut]
+ tmp[mask] = missing
diff --git a/src/tests/test_strixel.py b/src/tests/test_strixel.py
new file mode 100644
index 0000000000000000000000000000000000000000..8160f444ee36894ef212afb539f688e82430d3a0
--- /dev/null
+++ b/src/tests/test_strixel.py
@@ -0,0 +1,101 @@
+import numpy as np
+
+from calng import base_kernel_runner, utils
+
+
+pixel_data = np.arange(512 * 1024).reshape(512, 1024).astype(np.float32)
+
+
+def test_cols_A0123():
+ def convert_cols_A0123(data, out=None):
+ # https://redmine.xfel.eu/issues/126444
+ if out is None:
+ out = np.zeros((86, (1024 * 3 + 18)), dtype=np.float32)
+ ## 256 not divisible by 3, so we round up
+ ## 18 since we have 6 more pixels in H per gap
+ # firs we fill the normal pixels, the gap ones will be overwritten later
+ for yin in range(256):
+ for xin in range(1024):
+ ichip = int(xin / 256)
+ xout = (ichip * 774) + (xin % 256) * 3 + yin % 3
+ ## 774 is the chip period, 256*3+6
+ yout = int(yin / 3)
+ out[yout, xout] = data[yin, xin]
+ # now the gap pixels...
+ for igap in range(3):
+ for yin in range(256):
+ yout = int(yin / 6) * 2
+ # first the left side of gap
+ xin = igap * 256 + 255
+ xout = igap * 774 + 765 + yin % 6
+ out[yout, xout] = data[yin, xin]
+ out[yout + 1, xout] = data[yin, xin]
+ # then the right side is mirrored
+ xin = igap * 256 + 255 + 1
+ xout = igap * 774 + 765 + 11 - yin % 6
+ out[yout, xout] = data[yin, xin]
+ out[yout + 1, xout] = data[yin, xin]
+ # out[yout, xout] = out[yout, xout] / 2
+ # if we want a proper normalization (the area of those pixels is double, so they see 2x the signal)
+ return out
+
+ strixel_naive = convert_cols_A0123(pixel_data)
+ lut_package = np.load(
+ base_kernel_runner.kernel_dir / "strixel_cols_A0123-lut_mask.npz"
+ )
+ assert tuple(lut_package["frame_shape"]) == strixel_naive.shape
+ strixel_lut = np.zeros_like(strixel_naive)
+ utils.apply_partial_lut(
+ data=pixel_data,
+ lut=lut_package["lut"],
+ mask=lut_package["mask"],
+ out=strixel_lut,
+ missing=0,
+ )
+ assert np.array_equal(strixel_naive, strixel_lut)
+
+
+def test_rows_A1256():
+ def convert_rows_A1256(data, out=None):
+ # https://redmine.xfel.eu/issues/161148
+ if out is None:
+ out = np.zeros([*data.shape[:-2], 1488, 165], dtype=np.float32)
+
+ # Select only 4 center ASICS
+ data = data[..., 256:768]
+
+ # Offset due to guard ring pixels
+ offset_y = 9
+ offset_x = 11
+
+ for xin in range(
+ offset_x, 512 - 9
+ ): # on the right side, there are 9 guard ring pixels
+ # Bottom ASICs
+ for yin in range(offset_y, 255):
+ yout = (xin - offset_x) % 3 + (yin - offset_y) * 3
+ xout = (xin - offset_x) // 3 + (xin) // 257
+ out[..., yout, xout] = data[..., yin, xin]
+
+ # Top ASICs (mirrored on the horizontal axis)
+ for yin in range(257, 512 - offset_y):
+ yout = 2 - (xin - offset_x) % 3 + (yin - offset_y) * 3 + 6
+ xout = (xin - offset_x) // 3 + (xin) // 257
+ out[..., yout, xout] = data[..., yin, xin]
+
+ return out
+
+ strixel_naive = convert_rows_A1256(pixel_data)
+ lut_package = np.load(
+ base_kernel_runner.kernel_dir / "strixel_rows_A1256-lut_mask.npz"
+ )
+ assert tuple(lut_package["frame_shape"]) == strixel_naive.shape
+ strixel_lut = np.zeros_like(strixel_naive)
+ utils.apply_partial_lut(
+ data=pixel_data,
+ lut=lut_package["lut"],
+ mask=lut_package["mask"],
+ out=strixel_lut,
+ missing=0,
+ )
+ assert np.array_equal(strixel_naive, strixel_lut)