diff --git a/AWAControl/badger/environments/awa/__init__.py b/AWAControl/badger/environments/awa/__init__.py
index bc17a689d5291ac678e5d234fa4f5053db52d2be..2baaa6459d021820d1819f5107e2edad4b706154 100644
--- a/AWAControl/badger/environments/awa/__init__.py
+++ b/AWAControl/badger/environments/awa/__init__.py
@@ -24,10 +24,12 @@ class Environment(environment.Environment):
"Sy",
"Cx",
"Cy",
+ "correlation",
"total_size",
"bb_penalty",
"log10_total_intensity",
- "K1_phase_readback"
+ "K1_phase_readback",
+ "ICT1"
]
screen_name: str = None
image_diagnostic: Any = None
@@ -50,6 +52,8 @@ class Environment(environment.Environment):
results = {}
if "K1_phase_readback" in observable_names:
results["K1_phase_readback"] = caget("AWALLRF:K1:Phase")
+ if "ICT1" in observable_names:
+ results["ICT1"] = caget("AWAVXI11ICT:Ch1")
metadata = self.setpoints or {}
metadata = metadata | results
diff --git a/AWAControl/configs/awa_camera_config.yml b/AWAControl/configs/awa_camera_config.yml
index 55da523598ed277a209eb7f6ca3cae79cd707e4b..3144ff2f9f2c93f24f3a6ea2e5f0a10147aa26b5 100644
--- a/AWAControl/configs/awa_camera_config.yml
+++ b/AWAControl/configs/awa_camera_config.yml
@@ -36,12 +36,12 @@ DYG5:
radius: 206.9578032921999
video_number: 5
DYG7:
- type: blackfly
+ type: radhard
center:
- - 605
- - 548
- radius: 350
- ip_address: 192.168.2.106
+ - 618
+ - 558
+ radius: 500
+ serial_number: "138"
gain: 1.0
DYG14:
type: blackfly
@@ -90,10 +90,10 @@ EYG3:
DYG6:
type: frame_grabber
center:
- - 348.9857434881227
- - 237.18732942820998
+ - 398.9857434881227
+ - 337.18732942820998
radius: 196.96246822924843
- video_number: 36
+ video_number: 6
Slit_YAG:
type: frame_grabber
center:
diff --git a/AWAControl/diagnostics/screen.py b/AWAControl/diagnostics/screen.py
index 88e5f313d28fd40dea6b9ffcbbb8141c2e80fb5f..56c5490e29c039fc0e3411f3adc12424d5279e84 100644
--- a/AWAControl/diagnostics/screen.py
+++ b/AWAControl/diagnostics/screen.py
@@ -2,6 +2,7 @@ import json
import os.path
import sys
import time
+import scipy as sp
from copy import copy
from pprint import pprint
from time import sleep
@@ -51,6 +52,15 @@ def load_screen(camera_name: str):
roi=roi,
gain=camera_config["gain"], # 1.0 for 5 nC 5.0 for 1 nC,
)
+ elif camera_type == "radhard":
+ image_diagnostic = AWARadHardDiagnostic(
+ screen_name=camera_name,
+ serial_number=camera_config["serial_number"],
+ alias=camera_name,
+ resolution_suffix=None,
+ roi=roi,
+ gain=camera_config["gain"], # 1.0 for 5 nC 5.0 for 1 nC,
+ )
else:
raise RuntimeError(f"cannot load camera type {type}")
@@ -173,6 +183,7 @@ class AWAImageDiagnostic(BaseModel):
if result["Sx"] is not None:
result["Sx"] = result["Sx"] * self.resolution
result["Sy"] = result["Sy"] * self.resolution
+ result["correlation"] = result["correlation"] * self.resolution**2
else:
result = {}
@@ -344,6 +355,7 @@ class AWAImageDiagnostic(BaseModel):
"Cy": np.NaN,
"Sx": np.NaN,
"Sy": np.NaN,
+ "correlation": np.NaN,
"bb_penalty": np.NaN,
"total_intensity": 10**log10_total_intensity,
"log10_total_intensity": log10_total_intensity,
@@ -355,6 +367,7 @@ class AWAImageDiagnostic(BaseModel):
fits = self.fit_image(img)
centroid = fits["centroid"]
sizes = fits["rms_sizes"]
+ correlation = fits["correlation"]
# do analysis if fits return all good values
if np.all(~np.isnan(np.stack((centroid, sizes)))):
@@ -407,6 +420,7 @@ class AWAImageDiagnostic(BaseModel):
"Cy": centroid[1],
"Sx": sizes[0],
"Sy": sizes[1],
+ "correlation": correlation,
"bb_penalty": bb_penalty,
"total_intensity": fits["total_intensity"],
"log10_total_intensity": log10_total_intensity,
@@ -424,6 +438,7 @@ class AWAImageDiagnostic(BaseModel):
"Cy": np.NaN,
"Sx": np.NaN,
"Sy": np.NaN,
+ "correlation": np.NaN,
"bb_penalty": np.NaN,
"total_intensity": fits["total_intensity"],
"log10_total_intensity": log10_total_intensity,
@@ -435,23 +450,52 @@ class AWAImageDiagnostic(BaseModel):
return result
def fit_image(self, img):
- x_projection = np.sum(img, axis=0)
- y_projection = np.sum(img, axis=1)
-
+ x_projection = np.sum(img, axis=0)
+ y_projection = np.sum(img, axis=1)
+
+
# subtract min value from projections
- x_projection = x_projection - x_projection[:10].min()
- y_projection = y_projection - y_projection[:10].min()
-
+ x_projection = x_projection - x_projection[:10].min()
+ y_projection = y_projection - y_projection[:10].min()
+
+
para_x = fit_gaussian_linear_background(
x_projection, visualize=self.visualize, n_restarts=self.n_fitting_restarts
)
- para_y = fit_gaussian_linear_background(
+ para_y = fit_gaussian_linear_background(
y_projection, visualize=self.visualize, n_restarts=self.n_fitting_restarts
)
+
+ dim1,dim2=np.shape(img)
+ img45=sp.ndimage.interpolation.rotate(img,45,reshape=False,order=1)
+ if dim1==dim2:
+ img45_cropped=img45[int(dim1*(2-np.sqrt(2))/4):int(dim1*(2+np.sqrt(2))/4),int(dim2*(2-np.sqrt(2))/4):int(dim2*(2+np.sqrt(2))/4)]
+ elif dim1<dim2:
+ img45_cropped=img45[int(dim1*(2-np.sqrt(2))/4):int(dim1*(2+np.sqrt(2))/4),int(dim1*(2-np.sqrt(2))/4):int(dim1*(2+np.sqrt(2))/4)]
+ else:
+ img45_cropped=img45[int(dim2*(2-np.sqrt(2))/4):int(dim2*(2+np.sqrt(2))/4),int(dim2*(2-np.sqrt(2))/4):int(dim2*(2+np.sqrt(2))/4)]
+
+
+
+
+ x_projection45 = np.sum(img45_cropped, axis=0)
+ y_projection45 = np.sum(img45_cropped, axis=1)
+ x_projection45 = x_projection45 - x_projection45[:10].min()
+ y_projection45 = y_projection45 - y_projection45[:10].min()
+
+ para_x45 = fit_gaussian_linear_background(
+ x_projection45, visualize=self.visualize, n_restarts=self.n_fitting_restarts
+ )
+ para_y45 = fit_gaussian_linear_background(
+ y_projection45, visualize=self.visualize, n_restarts=self.n_fitting_restarts
+ )
+
+ para_xy=(para_x45[2]**2-para_y45[2]**2)/2
return {
"centroid": np.array((para_x[1], para_y[1])),
"rms_sizes": np.array((para_x[2], para_y[2])),
+ "correlation": para_xy,
"total_intensity": img.sum(),
"log10_total_intensity": np.log10(img.sum()),
}
@@ -538,6 +582,60 @@ class AWABlackflyDiagnostic(AWAImageDiagnostic):
img = img.reshape(ny, nx)
return img, extra_data
+
+class AWARadHardDiagnostic(AWAImageDiagnostic):
+ serial_number: str
+ gain: PositiveFloat = 1.0
+
+ @property
+ def pv_names(self) -> list:
+ suffixes = [
+ self.array_data_suffix,
+ self.array_n_cols_suffix,
+ self.array_n_rows_suffix,
+ ]
+ if self.resolution_suffix is not None:
+ suffixes += [self.resolution_suffix]
+
+ return [f"SI-138:{ele}" for ele in suffixes]
+
+ def set_camera(self):
+ print(f"setting camera {self.alias}")
+ caput("SI-138:cam1:Acquire", 0)
+ time.sleep(2)
+
+ print(str(caget("SI-138:cam1:GC_SetCameraName")))
+ current_ip = str(caget("SI-138:cam1:GC_SetCameraName"))
+
+ print(f"starting acquisition")
+ caput("SI-138:cam1:Acquire", 1)
+
+ def get_raw_data(self) -> (np.ndarray, dict):
+ if self.testing:
+ img = np.zeros((2000, 2000))
+ img[800:-800, 900:-900] = 1
+ self.resolution = 1.0
+ extra_data = {
+ "ICT1": np.random.randn() * 0.1 + 1.0,
+ "ICT2": np.random.randn() * 0.1 + 1.0,
+ }
+ else:
+ # get pvs
+ results = caget_many(self.pv_names)
+ if results[0] is None:
+ raise RuntimeError(
+ "epics returned no image values, make sure blackfly phobeus is active"
+ )
+ results[0] = np.uint16(results[0])
+
+ e_pvs = copy(self.extra_pvs)
+ if self.target_charge_pv is not None:
+ e_pvs += [self.target_charge_pv]
+ extra_data = dict(zip(e_pvs, caget_many(e_pvs)))
+ img, nx, ny = results[0], results[1], results[2]
+ img = img.reshape(ny, nx)
+
+ return img, extra_data
class AWAFrameGrabberDiagnostic(AWAImageDiagnostic):