From 30a97949a08265e6ab0aaaeefb82a647bfe10897 Mon Sep 17 00:00:00 2001
From: David Hammer <dhammer@mailbox.org>
Date: Thu, 20 Jan 2022 13:42:35 +0100
Subject: [PATCH] Remove unnecessary handling of gain mode
Assuming the detector sends appropriate data.gain values in fixed gain mode(s),
the GPU kernel can just run as normal. Constants will include three gain stages
- fixed gain is purely a constant retrieval parameter.
---
src/calng/JungfrauCorrection.py | 30 +++++++++++-------------------
1 file changed, 11 insertions(+), 19 deletions(-)
diff --git a/src/calng/JungfrauCorrection.py b/src/calng/JungfrauCorrection.py
index dddfd179..0cbe7fc8 100644
--- a/src/calng/JungfrauCorrection.py
+++ b/src/calng/JungfrauCorrection.py
@@ -17,23 +17,13 @@ from .base_correction import BaseCorrection, add_correction_step_schema
_pretend_pulse_table = np.arange(16, dtype=np.uint8)
+
class JungfrauConstants(enum.Enum):
Offset10Hz = enum.auto()
BadPixelsDark10Hz = enum.auto()
RelativeGain10Hz = enum.auto()
-class JungfrauGainMode(enum.IntEnum):
- # TODO: coordinate with pycalibration and check what is saved by control device
- # gain stages: G0, G1, G2 (plus secret HG0 - high CDS)
- dynamicgain = enum.auto() # default; uses G0-G2
- dynamichg0 = enum.auto() # uses HG0, G1, G2
- fixgain1 = enum.auto() # fix, use only G1
- fixgain2 = enum.auto() # fix, use only G2
- forceswitchg1 = enum.auto() # only for darks
- forceswitchg2 = enum.auto() # only for darks
-
-
class CorrectionFlags(enum.IntFlag):
NONE = 0
OFFSET = 1
@@ -55,7 +45,6 @@ class JungfrauGpuRunner(base_gpu.BaseGpuRunner):
output_data_dtype=cupy.float32,
bad_pixel_mask_value=cupy.nan,
burst_mode=False,
- gain_mode=JungfrauGainMode.dynamicgain,
):
self.burst_mode = burst_mode
self.input_shape = (memory_cells, pixels_y, pixels_x)
@@ -203,10 +192,15 @@ class JungfrauCalcatFriend(calcat_utils.BaseCalcatFriend):
STRING_ELEMENT(schema)
.key(f"{param_prefix}.gainMode")
- .description("Gain mode (WIP)")
+ .displayedName("Gain mode")
+ .description(
+ "Detector may be operating in one of several gain modes. For this "
+ "device to query appropriate constants, it is sufficient to know "
+ "whether gain mode is dynamic or fixed."
+ )
.assignmentOptional()
.defaultValue("dynamicgain")
- .options("dynamicgain,fixgain1,fixgain2")
+ .options("dynamicgain,fixedgain")
.commit(),
)
managed_keys.add(f"{param_prefix}.integrationTime")
@@ -226,9 +220,9 @@ class JungfrauCalcatFriend(calcat_utils.BaseCalcatFriend):
res["Integration Time"] = self._get_param("integrationTime")
res["Sensor Temperature"] = self._get_param("sensorTemperature")
res["Gain Setting"] = self._get_param("gainSetting")
- gain_mode = JungfrauGainMode[self._get_param("gainMode")]
- if gain_mode != 0: #is not JungfrauGainMode.dynamicgain:
- # TODO: figure out what to set
+ gain_mode = self._get_param("gainMode")
+ if gain_mode != "dynamicgain":
+ # NOTE: always include if CalCat is updated for this
res["Gain mode"] = 1
return res
@@ -300,7 +294,6 @@ class JungfrauCorrection(BaseCorrection):
def __init__(self, config):
super().__init__(config)
# TODO: gain mode as constant parameter and / or device configuration
- self.gain_mode = JungfrauGainMode[config.get("constantParameters.gainMode")]
try:
self.bad_pixel_mask_value = np.float32(
@@ -310,7 +303,6 @@ class JungfrauCorrection(BaseCorrection):
self.bad_pixel_mask_value = np.float32("nan")
self._kernel_runner_init_args = {
- "gain_mode": self.gain_mode,
"bad_pixel_mask_value": self.bad_pixel_mask_value,
"burst_mode": False, # TODO
}
--
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